Clustering of Behavioural Risk Factors in Jordan: A Stratified, Policy-Driven Analysis of WHO STEPS 2019

Clustering of Behavioural Risk Factors in Jordan: A Stratified, Policy-Driven Analysis of WHO STEPS 2019 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clustering of Behavioural Risk Factors in Jordan: A Stratified, Policy-Driven Analysis of WHO STEPS 2019 Mannat Tiwana, Gursimran Singh Walia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7281526/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Behavioural risk factors for non-communicable diseases (NCDs) often cluster within populations, increasing the risk of morbidity and mortality. Jordan faces a high burden of smoking, physical inactivity, and poor diet, yet national analyses have not examined how these risks co-occur across demographic groups. We used nationally representative data from the WHO STEPS 2019 survey in Jordan (n = 4,755) to assess the clustering of three behavioural risk factors: tobacco use, low fruit/vegetable intake, and physical inactivity. A composite risk score (0–3) was constructed. Descriptive statistics, logistic regression, and behaviour pairing analyses were stratified by sex, nationality (Jordanian vs Syrian), and region (North, Centre, South). 73.5% of adults had ≥ 2 behavioural risks, and 15.9% had all three. Clustering prevalence was highest in the North (74.7%), followed by Centre (73.2%) and South (69.7%). Among those with three risks, 69.4% were men; among those with two risks, 68.7% were women. Syrians were more concentrated in the two-risk category, while Jordanians dominated the three-risk group. Logistic regression showed women had 78.7% lower odds of being in the 3-risk group (OR = 0.213, p < .001), and Syrians had 21% lower odds than Jordanians (OR = 0.792, p = .007). The most common behavioural combinations were inactivity + poor diet (71.7%) and smoking + inactivity (74.8%). This is the first nationally representative study in Jordan to quantify behavioural risk clustering by region, sex, and nationality. Our findings reveal clear opportunities to design bundled interventions targeting common risk combinations, especially among men and in high-burden regions. Results are immediately relevant for national NCD planning and WHO HEARTS implementation in Jordan. behavioural risk factors syndemics clustering WHO STEPS Jordan refugee health NCDs public health policy Introduction Non-communicable diseases (NCDs) are the leading cause of morbidity and mortality worldwide, accounting for over 70% of global deaths annually, with a disproportionate burden in low- and middle-income countries (LMICs) [ 1 ]. In the Eastern Mediterranean Region (EMR), including Jordan, this burden is driven by high levels of modifiable behavioural risk factors such as tobacco use, physical inactivity, and poor diet [ 2 ]. These behaviours not only contribute individually to disease risk but often co-occur within individuals—a phenomenon referred to as behavioural risk clustering or syndemic burden [ 3 , 4 ]. The World Health Organization (WHO) and global public health frameworks increasingly recognize the need to address these risks in combination rather than isolation [ 5 ]. Integrated interventions that account for the syndemic nature of behavioural risks are more cost-effective and impactful, especially when tailored to specific population subgroups [ 6 ]. However, national-level data analyses in Jordan have traditionally focused on single risk factors or on the diagnosis of disease outcomes, without evaluating how behavioural risks cluster or differ by demographic characteristics such as sex, geographic region, or nationality. Jordan presents a unique context in which to examine behavioural clustering. As a middle-income country with one of the highest smoking rates in the EMR, and a host to a large Syrian refugee population, it is essential to understand how lifestyle risks distribute across diverse population groups [ 7 , 8 ]. The 2019 WHO STEPwise approach to NCD surveillance (STEPS) provided nationally representative data on key risk factors, but clustering analyses using this dataset remain absent from the literature. This study aims to fill that gap by using WHO STEPS 2019 data to: (1) quantify the extent of behavioural risk clustering in Jordan; (2) analyse how clustering varies by sex, region, and nationality; and (3) identify the most common pairwise combinations of risk factors. To our knowledge, this is the first nationally representative analysis in Jordan to explore stratified behavioural clustering and its implications for targeted, bundled NCD interventions. Methods This study used secondary data from the nationally representative 2019 WHO STEPwise Approach to Surveillance of Noncommunicable Disease Risk Factors (STEPS) in Jordan. The STEPS survey followed a multistage cluster sampling design to collect standardized data on behavioural, physical, and biochemical NCD risk factors from adults aged 18–69 years. Of the 5,713 individuals surveyed, 4,755 had complete data for all three behavioural risk variables and were included in this analysis. The three behavioural risk factors studied were current tobacco use, low fruit and vegetable intake, and physical inactivity. Smoking was self-reported based on daily or occasional use of tobacco products. Dietary intake was assessed through self-reported frequency of fruit and vegetable consumption, with insufficient intake defined as fewer than five combined servings per day, consistent with WHO recommendations [ 10 , 13 ]. Physical inactivity was determined from the frequency and duration of self-reported moderate and vigorous activity and classified according to WHO guidelines for insufficient physical activity [ 16 ]. Each risk factor was coded as a binary variable (1 = risk present, 0 = not present). A composite behavioural risk score was calculated by summing these variables, producing a cumulative score ranging from 0 (no risk factors) to 3 (all three risks present). This risk score was used to assess the degree of clustering. Analyses were stratified by sex (male, female), nationality (Jordanian, Syrian), and geographic region (North, Central, South), based on administrative divisions used in STEPS sampling. Descriptive statistics were computed for all variables. To assess predictors of extreme behavioural clustering (i.e., a score of 3), a binary logistic regression model was fitted. Predictor variables included sex, nationality, age, education, employment, and region. Results were reported as odds ratios (OR) with 95% confidence intervals (CI). Additionally, we conducted pairwise cross-tabulations to explore behavioural risk co-occurrence (e.g., smoking + inactivity). These behavioural pairings were stratified by sex and region to identify demographic and geographic patterns of risk clustering. All analyses were conducted using SPSS, and only valid, complete cases were included. No data imputation was performed. Patients and members of the public were not involved in the design, conduct, reporting, or dissemination plans of this research. This study used secondary, de-identified data from a publicly available dataset collected through WHO and Ministry of Health protocols [ 20 ]. Results Among the 4,755 adults with complete data on smoking, fruit and vegetable intake, and physical activity, the distribution of behavioural risk scores revealed that clustering was widespread (Table 1 ). Only 2.4% of participants had none of the three behavioural risks, while 24.0% had one risk factor, 57.6% had two, and 15.9% had all three. This indicates that nearly three-quarters of the adult population experienced two or more concurrent behavioural risks. Table 1 Distribution of Behavioural Risk Scores (n = 4,755) Risk Score Description Frequency (n) Percentage (%) 0 No behavioural risks 116 2.4% 1 One risk (smoking, diet, or inactivity) 1,142 24.0% 2 Two concurrent risks 2,739 57.6% 3 All three risks 758 15.9% We noticed risk clustering differed by sex (Table 2 ). Among participants with all three behavioural risks, 69.4% were men and 30.6% were women. In contrast, among those with two risks, 68.7% were women. This suggests that men were more likely to experience extreme clustering, while women tended to cluster in the two-risk category. Stratification by nationality revealed that Jordanians comprised 61.3% of those in the 3-risk group, while Syrians made up 38.7% (Table 3 ). However, in the 2-risk category, Syrians represented 46.9% of cases, suggesting early-stage clustering within this population [ 9 ]. Table 2 Behavioural Risk Clustering by Sex Sex 3 Risks (n, %) 2 Risks (n, %) Men 526 (69.4%) 858 (31.3%) Women 232 (30.6%) 1,881 (68.7%) Table 3 Risk Score by Nationality Nationality 3 Risks (%) 2 Risks (%) Jordanian 61.3% 53.1% Syrian 38.7% 46.9% Clustering also varied by geographic region (Table 6 ). The proportion of adults with two or more behavioural risks was highest in the North (74.7%), followed by the Central region (73.2%), and lowest in the South (69.7%) [ 14 ]. While all regions demonstrated high overall clustering, the North appeared to carry a marginally greater burden. Table 6 Regional Differences in Clustering (Risk Score ≥ 2) Region % with 2 + Risks North 74.7% Centre 73.2% South 69.7% Multivariable logistic regression was used to identify predictors of extreme clustering (i.e., a risk score of 3) (Table 4 ). Female sex was associated with significantly lower odds of having all three behavioural risks (OR = 0.213, 95% CI: 0.183–0.248, p < 0.001). Similarly, Syrian nationality was associated with 21% lower odds of 3-risk clustering compared to Jordanians (OR = 0.792, 95% CI: 0.675–0.930, p = 0.007). Age, education level, employment status, and region were not statistically significant predictors in the adjusted model [ 15 ]. Table 4 Logistic Regression – Predictors of High-Risk Clustering (Score = 3) Variable Odds Ratio (OR) 95% CI p-value Interpretation Female 0.213 0.183–0.248 < .001 78.7% lower odds than men Syrian 0.792 0.675–0.930 0.007 21% lower odds than Jordanians Age NS — — Not significant Education NS — — Not significant Employment NS — — Not significant Behavioural pairing analysis revealed consistent and meaningful patterns of co-occurrence (Table 5 ). Among smokers, 74.8% were also physically inactive. Among physically inactive individuals, 71.7% had low fruit and vegetable intake. This latter pairing was particularly pronounced among women, where 92.7% of inactive women also reported insufficient diet. Pairwise co-occurrence was consistently high across all regions, with the North and Central regions showing slightly higher clustering of smoking with inactivity [ 11 , 12 , 17 , 18 , 19 ]. Table 5 Behavioural Pairings (Overall) Pairing Prevalence (%) Notes Smoking + Inactivity 74.8% Among smokers Inactivity + Poor Diet 71.7% Among inactive individuals Inactivity + Diet (women) 92.7% Most common combo in women Taken together, these findings indicate that behavioural risk factors for NCDs in Jordan frequently co-occur, with distinct patterns by sex, nationality, and region. The clustering of inactivity with poor diet emerged as the most common and widespread combination, while smoking with inactivity was also prominent—particularly among men. These results underscore the need for bundled behavioural interventions that move beyond siloed approaches to risk factor prevention. Discussion This nationally representative analysis reveals that behavioural risk factor clustering is widespread among adults in Jordan, with over 73% experiencing two or more concurrent risks and nearly 16% having all three. To our knowledge, this is the first study to quantify the co-occurrence of smoking, poor diet, and physical inactivity in Jordan using WHO STEPS 2019 data, while also stratifying findings by sex, nationality, and region. These results underscore the importance of moving beyond siloed interventions and toward an integrated, bundled approach to non-communicable disease (NCD) prevention. Our findings are consistent with regional STEPS-based studies from Qatar, Vietnam, and Palestine, which report similar levels of clustering and emphasize the gendered nature of behavioural risk [ 1 – 3 ]. However, this study adds important nuance by demonstrating that women in Jordan tend to cluster in the two-risk group—most often inactivity combined with poor diet—while men dominate the high-risk (three-behaviour) cluster, typically combining smoking with inactivity. This sex-based divergence has direct implications for program design, particularly in tailoring behavioural interventions. The variation in clustering by nationality is another unique contribution. Jordanians were significantly more likely to fall into the highest-risk group compared to Syrian refugees. While Syrians had high levels of poor diet and inactivity, their lower tobacco use may partially explain this finding. However, their high representation in the two-risk group suggests a potential shift toward more severe clustering in the absence of early intervention. To date, most refugee health analyses in Jordan have focused on individual risk prevalence or access to care [ 4 , 5 ]; few have examined behavioural clustering as a syndemic burden. This analysis addresses that gap. Geographically, our study found the highest prevalence of clustering in the North and Centre regions. These areas not only contain dense urban populations but also host the majority of Syrian refugees. This supports the need for regionally targeted interventions that integrate behavioural counselling, food security policies, and physical activity promotion into community-level NCD prevention strategies. Our behavioural pairing analysis provides an additional layer of insight. Inactivity combined with poor diet was the most common pairing overall, affecting nearly three-quarters of inactive individuals. Among women, this pairing was even more prevalent, exceeding 90%. These findings mirror EMRO-wide data that suggest social, cultural, and economic barriers disproportionately affect women’s ability to engage in physical activity and maintain healthy diets [ 6 , 7 ]. Smoking paired with inactivity was the dominant pattern among men, reinforcing the need for integrated tobacco cessation and lifestyle counselling in primary care settings. The regression analysis highlighted that female sex and Syrian nationality were both independently associated with lower odds of extreme clustering. Age, education, and employment status were not significant predictors—a counterintuitive finding that challenges common assumptions and reinforces the importance of stratified behavioural surveillance. This study has important strengths. It is the first nationally representative analysis to examine behavioural risk clustering in Jordan using a composite risk score, stratified by both demographic and geographic dimensions. It introduces the first behavioural pairing matrix and offers practical insight for intervention design. The use of WHO-validated instruments and standardized survey methodology enhances the credibility and comparability of results. However, some limitations must be acknowledged. The cross-sectional nature of the STEPS survey precludes causal inference, and self-reported behaviours may be subject to recall or social desirability bias. Additionally, the dataset did not include metabolic outcomes, which limits our ability to model downstream effects such as hypertension or diabetes. Finally, urban-rural distinctions and refugee camp status were not available in the dataset, which may have masked important intra-regional variation. Despite these limitations, the findings have immediate policy relevance. Jordan’s NCD strategy, aligned with the WHO HEARTS framework, would benefit from embedding behavioural clustering analysis into future STEPS reporting and health system design. Interventions targeting diet and inactivity in women, smoking and inactivity in men, and early prevention for Syrians in high-cluster regions should be prioritized. Conclusion This study is the first to quantify behavioural risk clustering among adults in Jordan using nationally representative WHO STEPS 2019 data. The results demonstrate that clustering of modifiable risk factors—specifically smoking, physical inactivity, and poor diet—is highly prevalent and demographically patterned across sex, nationality, and geographic region. Clustering was especially high among men and Jordanians, while Syrian refugees showed early-stage behavioural overlap, particularly involving diet and inactivity. Women were more likely to experience dual risk, particularly inactivity and poor diet, while men exhibited higher triple-risk clustering. Regional disparities revealed that the North and Centre of the country carry the highest clustering burden. These findings highlight the urgent need to move beyond single-risk NCD interventions and adopt bundled, demographically tailored prevention strategies. Behavioural pairing analysis further supports the development of integrated programs—particularly those targeting smoking plus inactivity in men, and diet plus inactivity in women. National NCD strategies in Jordan should incorporate behavioural clustering analysis into routine surveillance and prioritize early intervention among groups most at risk for syndemic accumulation. Incorporating these insights into the design of preventive services can accelerate progress toward the WHO Global NCD Targets and support Jordan’s alignment with the HEARTS framework for primary care. Declarations Ethical Approval: The original STEPS survey protocol received approval from the Jordanian Ministry of Health and the WHO Ethics Review Committee. The secondary analysis used de-identified, publicly available data and required no further ethical review. Human Ethics and Consent to Participate Declarations Not applicable. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution MT conceived the study, conducted the data analysis, interpreted the findings, and GSW wrote the manuscript. Acknowledgments: The author gratefully acknowledges the World Health Organization and the Jordanian Ministry of Health for providing open access to the WHO STEPS 2019 dataset. Competing Interests : The author declares no competing interests. Data Availability: The dataset used in this study is publicly available from the World Health Organization STEPS website: https://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/steps/manuals References World Health Organization. (2019). 2019 STEPS Country Report: Jordan . Aloni K, Batieha A, Jaddou H, Khader Y, Abdo N, El-Khateeb M, Al-Nuaimat A. An alarmingly high and increasing prevalence of obesity in Jordan. Epidemiol Health. 2019;42:e2020040. International Diabetes Federation. (2024). Jordan Diabetes Data & Health Trends . Abdelhadi NN, Alsous MM. Prevalence and determinants of hypertension among women of childbearing age in Jordan. East Mediterr Health J. 2025;31(5):317–24. Khader YS, Batieha A, Jaddou H, El-Khateeb M, Ajlouni K. (2019). Hypertension in Jordan: Prevalence, awareness, control, and its associated factors. International Journal of Hypertension , 2019, 3210617. Ajlouni K, Khader YS, Batieha A, Ajlouni H, El-Khateeb M. Characterizing the type 2 diabetes mellitus epidemic in Jordan up to 2050. J Diabetes Metab Disord. 2020;19(1):1–9. World Health Organization. (2021). Jordan's National STEPwise Survey for Noncommunicable Diseases: Priorities and Measures . NCD Alliance. (2021). Jordan Puts NCDs at Heart of UHC and Humanitarian Response . Ansbro É, Homan T, Qasem J, Bil K, Tarawneh MR, Roberts B, Perel P, Jobanputra K. MSF experiences of providing multidisciplinary primary level NCD care for Syrian refugees and the host population in Jordan: An implementation study guided by the RE-AIM framework. BMC Health Serv Res. 2021;21(1):381. World Health Organization. (2020). Strengthening the Prevention and Management of Cardiovascular Disease Risk Factors Through Primary Health Care in Jordan . World Obesity Federation. (2025). Jordan Report Card . Habashneh MS, Al-Ali N. (2024). Prevalence and associated factors of non-communicable chronic diseases among academics at Mutah University in Jordan. PLoS ONE, 19(8), e0304829. World Health Organization. (2023). Nutrition Country Profile: Jordan . EMPHNET. (2021). Jordan Addresses Increasing NCD Burden Among Vulnerable Communities . Khader YS, Batieha A, Jaddou H, El-Khateeb M, Ajlouni K. (2021). Hypertension in Jordan: Prevalence, awareness, control, and its associated factors. International Journal of Hypertension , 2021, 3210617. World Health Organization. (2021). Surveillance of Noncommunicable Diseases: Jordan . World Health Organization. (2020). Prevention and Management of Mental Health Conditions in Jordan . World Health Organization. (2021). Jordan STEPS Survey 2019: Tobacco Fact Sheet . World Health Organization. (2021). Jordan STEPS Survey 2019: Fact Sheets . World Health Organization. (2021). Jordan STEPS Survey 2019: Data Reporting . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 Sep, 2025 Reviewers agreed at journal 11 Sep, 2025 Reviewers invited by journal 04 Sep, 2025 Editor invited by journal 08 Aug, 2025 Editor assigned by journal 07 Aug, 2025 Submission checks completed at journal 07 Aug, 2025 First submitted to journal 03 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7281526","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511901161,"identity":"552ac14a-1151-4ce2-ac6c-6405d6b90d3f","order_by":0,"name":"Mannat Tiwana","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBADHjD5oQJIMDM3EKEhwQCshXHGGZAWRuK0gClm3jawTvxadNvPHnvw88cfGYPjZw9+5p1XG83fDtTyo2IbTi1mZ/LSDXuADjM4k5csOXfb8dwZhxkbGHvO3Mat5UCOmQQPUAuQYSDxdtux3AagFmbGNjxazr8xk/wD0nL+jfEP3jnHcucT1HIjx0wabAuQIcnbUJO7gbCWN2bSMmnGPPZAhuWMYwdyNwK1HMTrl/NAw9/YyNlL9ucY3/hQU5c77/zhgw9+VODWgg4Og8kDRKsHgjpSFI+CUTAKRsEIAQBNTlwq+Uz2ygAAAABJRU5ErkJggg==","orcid":"","institution":"California State University","correspondingAuthor":true,"prefix":"","firstName":"Mannat","middleName":"","lastName":"Tiwana","suffix":""},{"id":511901162,"identity":"c83ba33a-5c51-4c09-8cef-a157a71433b6","order_by":1,"name":"Gursimran Singh Walia","email":"","orcid":"","institution":"Luxmi Bai Institute of Dental Sciences and Hospital, Baba Farid University of Health Sciences and Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gursimran","middleName":"Singh","lastName":"Walia","suffix":""}],"badges":[],"createdAt":"2025-08-03 06:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7281526/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7281526/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91119382,"identity":"6bfb7a29-dbde-4d49-a09e-f4138bb3b2be","added_by":"auto","created_at":"2025-09-11 18:30:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":473882,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7281526/v1/f0f4ebdb-616d-4092-a65a-8ca654a9ab90.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clustering of Behavioural Risk Factors in Jordan: A Stratified, Policy-Driven Analysis of WHO STEPS 2019","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNon-communicable diseases (NCDs) are the leading cause of morbidity and mortality worldwide, accounting for over 70% of global deaths annually, with a disproportionate burden in low- and middle-income countries (LMICs) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the Eastern Mediterranean Region (EMR), including Jordan, this burden is driven by high levels of modifiable behavioural risk factors such as tobacco use, physical inactivity, and poor diet [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These behaviours not only contribute individually to disease risk but often co-occur within individuals\u0026mdash;a phenomenon referred to as behavioural risk clustering or syndemic burden [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe World Health Organization (WHO) and global public health frameworks increasingly recognize the need to address these risks in combination rather than isolation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Integrated interventions that account for the syndemic nature of behavioural risks are more cost-effective and impactful, especially when tailored to specific population subgroups [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, national-level data analyses in Jordan have traditionally focused on single risk factors or on the diagnosis of disease outcomes, without evaluating how behavioural risks cluster or differ by demographic characteristics such as sex, geographic region, or nationality.\u003c/p\u003e\u003cp\u003eJordan presents a unique context in which to examine behavioural clustering. As a middle-income country with one of the highest smoking rates in the EMR, and a host to a large Syrian refugee population, it is essential to understand how lifestyle risks distribute across diverse population groups [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The 2019 WHO STEPwise approach to NCD surveillance (STEPS) provided nationally representative data on key risk factors, but clustering analyses using this dataset remain absent from the literature.\u003c/p\u003e\u003cp\u003eThis study aims to fill that gap by using WHO STEPS 2019 data to: (1) quantify the extent of behavioural risk clustering in Jordan; (2) analyse how clustering varies by sex, region, and nationality; and (3) identify the most common pairwise combinations of risk factors. To our knowledge, this is the first nationally representative analysis in Jordan to explore stratified behavioural clustering and its implications for targeted, bundled NCD interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study used secondary data from the nationally representative 2019 WHO STEPwise Approach to Surveillance of Noncommunicable Disease Risk Factors (STEPS) in Jordan. The STEPS survey followed a multistage cluster sampling design to collect standardized data on behavioural, physical, and biochemical NCD risk factors from adults aged 18\u0026ndash;69 years. Of the 5,713 individuals surveyed, 4,755 had complete data for all three behavioural risk variables and were included in this analysis.\u003c/p\u003e\u003cp\u003eThe three behavioural risk factors studied were current tobacco use, low fruit and vegetable intake, and physical inactivity. Smoking was self-reported based on daily or occasional use of tobacco products. Dietary intake was assessed through self-reported frequency of fruit and vegetable consumption, with insufficient intake defined as fewer than five combined servings per day, consistent with WHO recommendations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Physical inactivity was determined from the frequency and duration of self-reported moderate and vigorous activity and classified according to WHO guidelines for insufficient physical activity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEach risk factor was coded as a binary variable (1\u0026thinsp;=\u0026thinsp;risk present, 0\u0026thinsp;=\u0026thinsp;not present). A composite behavioural risk score was calculated by summing these variables, producing a cumulative score ranging from 0 (no risk factors) to 3 (all three risks present). This risk score was used to assess the degree of clustering.\u003c/p\u003e\u003cp\u003eAnalyses were stratified by sex (male, female), nationality (Jordanian, Syrian), and geographic region (North, Central, South), based on administrative divisions used in STEPS sampling. Descriptive statistics were computed for all variables. To assess predictors of extreme behavioural clustering (i.e., a score of 3), a binary logistic regression model was fitted. Predictor variables included sex, nationality, age, education, employment, and region. Results were reported as odds ratios (OR) with 95% confidence intervals (CI).\u003c/p\u003e\u003cp\u003eAdditionally, we conducted pairwise cross-tabulations to explore behavioural risk co-occurrence (e.g., smoking\u0026thinsp;+\u0026thinsp;inactivity). These behavioural pairings were stratified by sex and region to identify demographic and geographic patterns of risk clustering. All analyses were conducted using SPSS, and only valid, complete cases were included. No data imputation was performed.\u003c/p\u003e\u003cp\u003ePatients and members of the public were not involved in the design, conduct, reporting, or dissemination plans of this research. This study used secondary, de-identified data from a publicly available dataset collected through WHO and Ministry of Health protocols [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the 4,755 adults with complete data on smoking, fruit and vegetable intake, and physical activity, the distribution of behavioural risk scores revealed that clustering was widespread (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Only 2.4% of participants had none of the three behavioural risks, while 24.0% had one risk factor, 57.6% had two, and 15.9% had all three. This indicates that nearly three-quarters of the adult population experienced two or more concurrent behavioural risks.\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\u003eDistribution of Behavioural Risk Scores (n\u0026thinsp;=\u0026thinsp;4,755)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\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\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo behavioural risks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne risk (smoking, diet, or inactivity)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTwo concurrent risks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll three risks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.9%\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\u003eWe noticed risk clustering differed by sex (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among participants with all three behavioural risks, 69.4% were men and 30.6% were women. In contrast, among those with two risks, 68.7% were women. This suggests that men were more likely to experience extreme clustering, while women tended to cluster in the two-risk category. Stratification by nationality revealed that Jordanians comprised 61.3% of those in the 3-risk group, while Syrians made up 38.7% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, in the 2-risk category, Syrians represented 46.9% of cases, suggesting early-stage clustering within this population [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\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 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBehavioural Risk Clustering by Sex\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 Risks (n, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 Risks (n, %)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e526 (69.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e858 (31.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e232 (30.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,881 (68.7%)\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\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\u003eRisk Score by Nationality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNationality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 Risks (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 Risks (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJordanian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSyrian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.9%\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\u003eClustering also varied by geographic region (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The proportion of adults with two or more behavioural risks was highest in the North (74.7%), followed by the Central region (73.2%), and lowest in the South (69.7%) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. While all regions demonstrated high overall clustering, the North appeared to carry a marginally greater burden.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegional Differences in Clustering (Risk Score\u0026thinsp;\u0026ge;\u0026thinsp;2)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e% with 2\u0026thinsp;+\u0026thinsp;Risks\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentre\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e73.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69.7%\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\u003eMultivariable logistic regression was used to identify predictors of extreme clustering (i.e., a risk score of 3) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Female sex was associated with significantly lower odds of having all three behavioural risks (OR\u0026thinsp;=\u0026thinsp;0.213, 95% CI: 0.183\u0026ndash;0.248, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, Syrian nationality was associated with 21% lower odds of 3-risk clustering compared to Jordanians (OR\u0026thinsp;=\u0026thinsp;0.792, 95% CI: 0.675\u0026ndash;0.930, p\u0026thinsp;=\u0026thinsp;0.007). Age, education level, employment status, and region were not statistically significant predictors in the adjusted model [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic Regression \u0026ndash; Predictors of High-Risk Clustering (Score\u0026thinsp;=\u0026thinsp;3)\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\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOdds Ratio (OR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.183\u0026ndash;0.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78.7% lower odds than men\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSyrian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.675\u0026ndash;0.930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21% lower odds than Jordanians\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot significant\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\u003eBehavioural pairing analysis revealed consistent and meaningful patterns of co-occurrence (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Among smokers, 74.8% were also physically inactive. Among physically inactive individuals, 71.7% had low fruit and vegetable intake. This latter pairing was particularly pronounced among women, where 92.7% of inactive women also reported insufficient diet. Pairwise co-occurrence was consistently high across all regions, with the North and Central regions showing slightly higher clustering of smoking with inactivity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBehavioural Pairings (Overall)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePairing\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrevalence (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNotes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u0026thinsp;+\u0026thinsp;Inactivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAmong smokers\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInactivity\u0026thinsp;+\u0026thinsp;Poor Diet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAmong inactive individuals\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInactivity\u0026thinsp;+\u0026thinsp;Diet (women)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMost common combo in women\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\u003eTaken together, these findings indicate that behavioural risk factors for NCDs in Jordan frequently co-occur, with distinct patterns by sex, nationality, and region. The clustering of inactivity with poor diet emerged as the most common and widespread combination, while smoking with inactivity was also prominent\u0026mdash;particularly among men. These results underscore the need for bundled behavioural interventions that move beyond siloed approaches to risk factor prevention.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis nationally representative analysis reveals that behavioural risk factor clustering is widespread among adults in Jordan, with over 73% experiencing two or more concurrent risks and nearly 16% having all three. To our knowledge, this is the first study to quantify the co-occurrence of smoking, poor diet, and physical inactivity in Jordan using WHO STEPS 2019 data, while also stratifying findings by sex, nationality, and region. These results underscore the importance of moving beyond siloed interventions and toward an integrated, bundled approach to non-communicable disease (NCD) prevention.\u003c/p\u003e\u003cp\u003eOur findings are consistent with regional STEPS-based studies from Qatar, Vietnam, and Palestine, which report similar levels of clustering and emphasize the gendered nature of behavioural risk [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, this study adds important nuance by demonstrating that women in Jordan tend to cluster in the two-risk group\u0026mdash;most often inactivity combined with poor diet\u0026mdash;while men dominate the high-risk (three-behaviour) cluster, typically combining smoking with inactivity. This sex-based divergence has direct implications for program design, particularly in tailoring behavioural interventions.\u003c/p\u003e\u003cp\u003eThe variation in clustering by nationality is another unique contribution. Jordanians were significantly more likely to fall into the highest-risk group compared to Syrian refugees. While Syrians had high levels of poor diet and inactivity, their lower tobacco use may partially explain this finding. However, their high representation in the two-risk group suggests a potential shift toward more severe clustering in the absence of early intervention. To date, most refugee health analyses in Jordan have focused on individual risk prevalence or access to care [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; few have examined behavioural clustering as a syndemic burden. This analysis addresses that gap.\u003c/p\u003e\u003cp\u003eGeographically, our study found the highest prevalence of clustering in the North and Centre regions. These areas not only contain dense urban populations but also host the majority of Syrian refugees. This supports the need for regionally targeted interventions that integrate behavioural counselling, food security policies, and physical activity promotion into community-level NCD prevention strategies.\u003c/p\u003e\u003cp\u003eOur behavioural pairing analysis provides an additional layer of insight. Inactivity combined with poor diet was the most common pairing overall, affecting nearly three-quarters of inactive individuals. Among women, this pairing was even more prevalent, exceeding 90%. These findings mirror EMRO-wide data that suggest social, cultural, and economic barriers disproportionately affect women\u0026rsquo;s ability to engage in physical activity and maintain healthy diets [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Smoking paired with inactivity was the dominant pattern among men, reinforcing the need for integrated tobacco cessation and lifestyle counselling in primary care settings.\u003c/p\u003e\u003cp\u003eThe regression analysis highlighted that female sex and Syrian nationality were both independently associated with lower odds of extreme clustering. Age, education, and employment status were not significant predictors\u0026mdash;a counterintuitive finding that challenges common assumptions and reinforces the importance of stratified behavioural surveillance.\u003c/p\u003e\u003cp\u003eThis study has important strengths. It is the first nationally representative analysis to examine behavioural risk clustering in Jordan using a composite risk score, stratified by both demographic and geographic dimensions. It introduces the first behavioural pairing matrix and offers practical insight for intervention design. The use of WHO-validated instruments and standardized survey methodology enhances the credibility and comparability of results.\u003c/p\u003e\u003cp\u003eHowever, some limitations must be acknowledged. The cross-sectional nature of the STEPS survey precludes causal inference, and self-reported behaviours may be subject to recall or social desirability bias. Additionally, the dataset did not include metabolic outcomes, which limits our ability to model downstream effects such as hypertension or diabetes. Finally, urban-rural distinctions and refugee camp status were not available in the dataset, which may have masked important intra-regional variation.\u003c/p\u003e\u003cp\u003eDespite these limitations, the findings have immediate policy relevance. Jordan\u0026rsquo;s NCD strategy, aligned with the WHO HEARTS framework, would benefit from embedding behavioural clustering analysis into future STEPS reporting and health system design. Interventions targeting diet and inactivity in women, smoking and inactivity in men, and early prevention for Syrians in high-cluster regions should be prioritized.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study is the first to quantify behavioural risk clustering among adults in Jordan using nationally representative WHO STEPS 2019 data. The results demonstrate that clustering of modifiable risk factors\u0026mdash;specifically smoking, physical inactivity, and poor diet\u0026mdash;is highly prevalent and demographically patterned across sex, nationality, and geographic region.\u003c/p\u003e\u003cp\u003eClustering was especially high among men and Jordanians, while Syrian refugees showed early-stage behavioural overlap, particularly involving diet and inactivity. Women were more likely to experience dual risk, particularly inactivity and poor diet, while men exhibited higher triple-risk clustering. Regional disparities revealed that the North and Centre of the country carry the highest clustering burden.\u003c/p\u003e\u003cp\u003eThese findings highlight the urgent need to move beyond single-risk NCD interventions and adopt bundled, demographically tailored prevention strategies. Behavioural pairing analysis further supports the development of integrated programs\u0026mdash;particularly those targeting smoking plus inactivity in men, and diet plus inactivity in women.\u003c/p\u003e\u003cp\u003eNational NCD strategies in Jordan should incorporate behavioural clustering analysis into routine surveillance and prioritize early intervention among groups most at risk for syndemic accumulation. Incorporating these insights into the design of preventive services can accelerate progress toward the WHO Global NCD Targets and support Jordan\u0026rsquo;s alignment with the HEARTS framework for primary care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthical Approval:\u003c/h2\u003e\u003cp\u003e The original STEPS survey protocol received approval from the Jordanian Ministry of Health and the WHO Ethics Review Committee. The secondary analysis used de-identified, publicly available data and required no further ethical review.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate Declarations\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMT conceived the study, conducted the data analysis, interpreted the findings, and GSW wrote the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e\u003cp\u003eThe author gratefully acknowledges the World Health Organization and the Jordanian Ministry of Health for providing open access to the WHO STEPS 2019 dataset.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCompeting Interests\u003c/b\u003e: The author declares no competing interests.\u003c/p\u003e\u003ch2\u003eData Availability:\u003c/h2\u003e\u003cp\u003eThe dataset used in this study is publicly available from the World Health Organization STEPS website: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/steps/manuals\u003c/span\u003e\u003cspan address=\"https://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/steps/manuals\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. (2019). \u003cem\u003e2019 STEPS Country Report: Jordan\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAloni K, Batieha A, Jaddou H, Khader Y, Abdo N, El-Khateeb M, Al-Nuaimat A. An alarmingly high and increasing prevalence of obesity in Jordan. Epidemiol Health. 2019;42:e2020040.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInternational Diabetes Federation. (2024). \u003cem\u003eJordan Diabetes Data \u0026amp; Health Trends\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdelhadi NN, Alsous MM. Prevalence and determinants of hypertension among women of childbearing age in Jordan. East Mediterr Health J. 2025;31(5):317\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhader YS, Batieha A, Jaddou H, El-Khateeb M, Ajlouni K. (2019). Hypertension in Jordan: Prevalence, awareness, control, and its associated factors. \u003cem\u003eInternational Journal of Hypertension\u003c/em\u003e, 2019, 3210617.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAjlouni K, Khader YS, Batieha A, Ajlouni H, El-Khateeb M. Characterizing the type 2 diabetes mellitus epidemic in Jordan up to 2050. J Diabetes Metab Disord. 2020;19(1):1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. (2021). \u003cem\u003eJordan's National STEPwise Survey for Noncommunicable Diseases: Priorities and Measures\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNCD Alliance. (2021). \u003cem\u003eJordan Puts NCDs at Heart of UHC and Humanitarian Response\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnsbro \u0026Eacute;, Homan T, Qasem J, Bil K, Tarawneh MR, Roberts B, Perel P, Jobanputra K. MSF experiences of providing multidisciplinary primary level NCD care for Syrian refugees and the host population in Jordan: An implementation study guided by the RE-AIM framework. BMC Health Serv Res. 2021;21(1):381.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. (2020). \u003cem\u003eStrengthening the Prevention and Management of Cardiovascular Disease Risk Factors Through Primary Health Care in Jordan\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Obesity Federation. (2025). \u003cem\u003eJordan Report Card\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHabashneh MS, Al-Ali N. (2024). Prevalence and associated factors of non-communicable chronic diseases among academics at Mutah University in Jordan. PLoS ONE, 19(8), e0304829.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. (2023). \u003cem\u003eNutrition Country Profile: Jordan\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEMPHNET. (2021). \u003cem\u003eJordan Addresses Increasing NCD Burden Among Vulnerable Communities\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhader YS, Batieha A, Jaddou H, El-Khateeb M, Ajlouni K. (2021). Hypertension in Jordan: Prevalence, awareness, control, and its associated factors. \u003cem\u003eInternational Journal of Hypertension\u003c/em\u003e, 2021, 3210617.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. (2021). \u003cem\u003eSurveillance of Noncommunicable Diseases: Jordan\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. (2020). \u003cem\u003ePrevention and Management of Mental Health Conditions in Jordan\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. (2021). \u003cem\u003eJordan STEPS Survey 2019: Tobacco Fact Sheet\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. (2021). \u003cem\u003eJordan STEPS Survey 2019: Fact Sheets\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. (2021). \u003cem\u003eJordan STEPS Survey 2019: Data Reporting\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"behavioural risk factors, syndemics, clustering, WHO STEPS, Jordan, refugee health, NCDs, public health policy","lastPublishedDoi":"10.21203/rs.3.rs-7281526/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7281526/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBehavioural risk factors for non-communicable diseases (NCDs) often cluster within populations, increasing the risk of morbidity and mortality. Jordan faces a high burden of smoking, physical inactivity, and poor diet, yet national analyses have not examined how these risks co-occur across demographic groups.\u003c/p\u003e\u003cp\u003eWe used nationally representative data from the WHO STEPS 2019 survey in Jordan (n\u0026thinsp;=\u0026thinsp;4,755) to assess the clustering of three behavioural risk factors: tobacco use, low fruit/vegetable intake, and physical inactivity. A composite risk score (0\u0026ndash;3) was constructed. Descriptive statistics, logistic regression, and behaviour pairing analyses were stratified by sex, nationality (Jordanian vs Syrian), and region (North, Centre, South).\u003c/p\u003e\u003cp\u003e73.5% of adults had\u0026thinsp;\u0026ge;\u0026thinsp;2 behavioural risks, and 15.9% had all three. Clustering prevalence was highest in the North (74.7%), followed by Centre (73.2%) and South (69.7%). Among those with three risks, 69.4% were men; among those with two risks, 68.7% were women. Syrians were more concentrated in the two-risk category, while Jordanians dominated the three-risk group. Logistic regression showed women had 78.7% lower odds of being in the 3-risk group (OR\u0026thinsp;=\u0026thinsp;0.213, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and Syrians had 21% lower odds than Jordanians (OR\u0026thinsp;=\u0026thinsp;0.792, p\u0026thinsp;=\u0026thinsp;.007). The most common behavioural combinations were inactivity\u0026thinsp;+\u0026thinsp;poor diet (71.7%) and smoking\u0026thinsp;+\u0026thinsp;inactivity (74.8%).\u003c/p\u003e\u003cp\u003eThis is the first nationally representative study in Jordan to quantify behavioural risk clustering by region, sex, and nationality. Our findings reveal clear opportunities to design bundled interventions targeting common risk combinations, especially among men and in high-burden regions. Results are immediately relevant for national NCD planning and WHO HEARTS implementation in Jordan.\u003c/p\u003e","manuscriptTitle":"Clustering of Behavioural Risk Factors in Jordan: A Stratified, Policy-Driven Analysis of WHO STEPS 2019","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 18:13:55","doi":"10.21203/rs.3.rs-7281526/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-09-18T05:05:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119893852054499790709033252248014353291","date":"2025-09-11T05:22:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-04T13:48:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-08T08:46:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-07T06:55:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-07T06:54:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-08-03T06:00:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d177674d-f9a0-4857-8d9d-d3771183dfb1","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-11T18:13:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 18:13:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7281526","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7281526","identity":"rs-7281526","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}