Exploring Socio-Demographic Determinants of Obesity in Jordanian Women of Reproductive Age: Insights from a Nationwide Survey

Exploring Socio-Demographic Determinants of Obesity in Jordanian Women of Reproductive Age: Insights from a Nationwide Survey | 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 Exploring Socio-Demographic Determinants of Obesity in Jordanian Women of Reproductive Age: Insights from a Nationwide Survey Mahmoud Shaaban Abdelgalil, Sara Hosny El-Farargy, Mohamed Adel Dowidar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5402078/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jan, 2025 Read the published version in BMC Public Health → Version 1 posted 14 You are reading this latest preprint version Abstract Aim: We aimed to explore the predictors associated with obesity among adult ever-married Jordanian women aged 20–49 years based on the Jordanian Population and Family Health Survey (JPFHS). Method: Our study analyzed data from the JPFHS conducted in 2017-18, which initially included 14,689 ever-married women. We performed both univariate and multivariable analyses to determine the socio-demographic predictors of obesity among these women. Result: We included 4,226 Jordanian women in our study, of whom 2,170 were classified as obese and 2,056 had a normal body mass index (BMI). Multivariable analysis indicated that increasing age and living in Tafilh were significantly associated with higher odds of developing obesity (p < 0.05). Conversely, factors such as being in the wealthiest category, residing in Maan and Aqaba, and smoking every day were significantly linked to reduced odds of obesity (p < 0.05). Additionally, no significant associations were found between obesity development and variables such as the type of place of residence, educational level, frequency of reading newspapers or magazines, radio listening, television watching, or internet use in the past month (p > 0.05). Conclusion: Appropriate and targeted interventions should be developed for Jordanian women to address obesity and its related health issues. Policymakers should adopt a multilevel approach that focuses on high-risk subgroups, including older women, and those living in Tafilh. Efforts should be made to raise awareness and provide preventative measures tailored to these groups to effectively reduce obesity and its associated complications. Obesity Women Jordan DHS JPFHS 1. Introduction Obesity has become a critical public health challenge globally, particularly in low- and middle-income countries undergoing rapid socioeconomic transitions( 1 ). The World Health Organization defines obesity as an excessive fat accumulation that poses significant health risks ( 2 ). Over recent decades, global obesity rates have escalated dramatically, with the Middle East, including Jordan, experiencing a notable increase( 3 ). Current estimates indicate that approximately 2 billion adults worldwide are overweight, with 677.6 million classified as obese ( 4 ). Women represent a significant proportion of this group, comprising 393.5 million of the global obese population ( 5 ). Obesity in women is associated with numerous adverse health outcomes, including increased risks of cardiovascular diseases, type 2 diabetes, and various malignancies, such as breast, ovarian, and endometrial cancers( 6 – 8 ). These conditions contribute to premature mortality, reduced productivity, economic burdens on healthcare systems, and psychological distress( 9 , 10 ). The COVID-19 pandemic further underscored the vulnerability of obese individuals, demonstrating an elevated risk of severe outcomes, including hospitalization and mortality. A recent meta-analysis of 75 international studies found that obese adults face a 113% higher risk of hospitalization, a 74% higher risk of ICU admission, and a 48% increased risk of death compared to those with normal weight ( 11 ). This escalating obesity crisis imposes a significant strain on healthcare systems and carries a global economic burden of approximately $ 2 trillion annually( 12 ). Additionally, maternal obesity is associated with a higher likelihood of obesity in offspring, perpetuating an intergenerational cycle of obesity( 13 , 14 ). While overweight and obesity were once considered problems exclusive to high-income countries, they have become global issues, impacting low- and middle-income nations as well, including those in the Eastern Mediterranean Region (EMR) ( 6 , 15 ). Within the EMR, which includes Jordan, obesity rates are alarmingly high, significantly contributing to the burden of non-communicable diseases. In Jordan, the rising prevalence of obesity among adults presents a significant challenge to the healthcare system ( 3 ). Obesity rates are notably higher among females, at 43.1%, compared to 28.2% among males( 16 ). Over the past two decades, this prevalence has increased at an average annual rate of 1.38% ( 16 ). Key factors contributing to this trend include dietary shifts, sedentary lifestyles, urbanization, marital status, and cultural norms ( 3 ). Musaiger et al.'s systematic review of obesity in the EMR identified several key factors that contribute to the increased risk of obesity in the region. These factors include dietary shifts, physical inactivity, urbanization, marital status, shorter breastfeeding durations, frequent snacking, skipping breakfast, high consumption of sugary drinks, increased dining out, prolonged television viewing, aggressive marketing of high-fat foods, stunting, body image perceptions, cultural influences, and food subsidy policies( 17 ).Another study using data from the Jordan Population and Family Health Survey (JPFHS) 2009 reported that factors such as age, residence in the southern regions of Jordan, early marriage, parity, wealth status, and smoking are associated with obesity over time( 18 ). Despite existing research, there is a lack of recent evidence on the socioeconomic, behavioral, and reproductive factors linked to obesity. This study aims to investigate the socio-demographic correlates of obesity among adult ever-married women in Jordan by utilizing updated datasets from the JPFHS 2017-18. 2. Methods 2.1 Data Source This study utilized data from the JPFHS conducted in 2017-18. The survey included a nationally representative sample of women aged 15–49 years across Jordan's 12 governorates. Anthropometric measurements, including body mass index (BMI), were collected to assess the nutritional status of the participants. 2.2 Inclusion Criteria The study focused on adult ever-married women aged 20–49 years who had complete BMI data. Women with a BMI measured according to the Centers for Disease Control and Prevention guidelines were included( 19 ). 2.3 Exclusion Criteria Women who had missing BMI data, those classified as underweight (BMI < 18.5), overweight (BMI 25–30), or women aged below 20 years were excluded from the analysis. 2.4 Included Variables In our cross-sectional study, we analyzed a variety of sociodemographic and behavioral variables to assess their association with obesity among Jordanian women of reproductive age. Age was categorized into six 5-year groups: 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years. The type of place of residence was classified as either urban or rural. Educational level was divided into four categories: no education, primary, secondary, and higher. The wealth index was grouped into five categories: poorest, poorer, middle, richer, and richest. Regional analysis was conducted across three major regions: Central (Amman, Balqa, Zarqua, Madaba), Northern (Irbid, Mafraq, Jerash, Aljoun), and Southern (Karak, Tafilh, Maan, Aqaba). We also examined the frequency of media consumption, including reading newspapers or magazines, listening to the radio, and watching television, each categorized as not at all, less than once a week, or at least once a week. Household characteristics included whether the household owned a car or truck (yes/no) and the type of cooking fuel used, categorized as electricity, natural gas, kerosene, coal/lignite, or no food cooked in-house. Internet usage over the past month was assessed as not at all, less than once a week, at least once a week, or almost every day. Finally, we evaluated the current contraceptive method used by participants, including options such as not using any method, pill, IUD, injections, male condom, female sterilization, male sterilization, periodic abstinence, withdrawal, implants/Norplant, and lactational amenorrhea (LAM). 2.5 Statistical Analysis Data were analyzed using SPSS version 24. We used a weighted count for the analysis based on DHS recommendations( 20 ). The sample weight was an eight-digit variable with six implied decimal places, and the weighting factor was divided by 1,000,000 for application. Descriptive statistics were used to summarize the characteristics of the study population, with results reported as frequencies and percentages. Multivariable logistic regression analyses were performed to identify predictors of obesity. The results were presented as adjusted odds ratios (AORs) with 95% confidence intervals (CIs). A p-value of less than 0.05 was considered statistically significant. 3. Results Data were collected from 4,226 Jordanian women of reproductive age (20–49 years). Of these participants, 2,170 were classified as obese, while 2,056 had a normal BMI ( Table 1 ). Table 1 Characteristics of the included women according to BMI Variables BMI Normal (BMI = 18.5: 25) N = 2056 Obese (BMI > 30) N = 2170 Count N % Count N % Age in 5-year groups 20–24 343 (16.7%) 116 (5.3%) 25–29 509 (24.8%) 177 (8.1%) 30–34 445 (21.6%) 312 (14.4%) 35–39 306 (14.9%) 404 (18.6%) 40–44 257 (12.5%) 559 (25.8%) 45–49 197 (9.6%) 603 (27.8%) Type of place of residence Urban 1931 (89.6%) 1927 (88.2%) Rural 223 (10.4%) 258 (11.8%) Highest educational level No education 52 (2.4%) 59 (2.7%) Primary 108 (5.0%) 180 (8.2%) Secondary 1177 (54.6%) 1327 (60.8%) Higher 817 (37.9%) 619 (28.3%) Wealth index combined Poorest 448 (20.8%) 481 (22.0%) Poorer 394 (18.3%) 473 (21.6%) Middle 464 (21.5%) 470 (21.5%) Richer 416 (19.3%) 456 (20.9%) Richest 432 (20.0%) 305 (14.0%) Region Central Region Amman 860 (39.9%) 815 (37.3%) Balqa 131 (6.1%) 103 (4.7%) Zarqua 272 (12.6%) 348 (15.9%) Madaba 44 (2.1%) 57 (2.6%) North Region Irbid 369 (17.1%) 418 (19.1%) Mafraq 130 (6.0%) 159 (7.3%) Jerash 63 (2.9%) 66 (3.0%) Aljoun 51 (2.4%) 48 (2.2%) South Region Karak 80 (3.7%) 75 (3.4%) Tafilh 21 (1.0%) 47 (2.2%) Maan 54 (2.5%) 23 (1.0%) Aquaba 81 (3.8%) 26 (1.2%) Frequency of reading newspaper or magazine Not at all 1243 (57.7%) 1320 (60.4%) Less than once a week 460 (21.4%) 430 (19.7%) At least once a week 451 (20.9%) 436 (19.9%) Frequency of listening to radio Not at all 1134 (52.7%) 1179 (53.9%) Less than once a week 502 (23.3%) 504 (23.1%) At least once a week 518 (24.0%) 502 (23.0%) Frequency of watching television Not at all 197 (9.1%) 190 (8.7%) Less than once a week 355 (16.5%) 357 (16.3%) At least once a week 1603 (74.4%) 1638 (74.9%) Frequency of using the Internet last month Not at all 425 (19.7%) 598 (27.4%) Less than once a week 49 (2.3%) 61 (2.8%) At least once a week 175 (8.1%) 186 (8.5%) Almost every day 1505 (69.9%) 1340 (61.3%) Frequency smokes cigarettes Do not smoke 1945 (90.3%) 2029 (92.9%) Every day 139 (6.5%) 110 (5.0%) Some days 71 (3.3%) 46 (2.1%) The analysis revealed that the highest proportion of obese women was in the 40–49 age group, accounting for 53.6% of the total obese population. In contrast, 46.4% of women with a normal BMI were between 25–34 age groups. Regarding residence, 88.2% of women with a normal BMI and 89.6% of obese women lived in urban areas. Among women with a normal BMI, over half had completed secondary education, while 5.0% had completed primary education, 37.9% had higher education, and 2.4% had no formal education. In the obese group, 60.8% had secondary education, 8.2% had primary education, 28.3% had higher education, and 2.7% had no formal education. Obesity was least prevalent among women in the richest wealth quintile, affecting 14.0% of women in this group. In comparison, obesity rates were higher in the richer (20.9%), middle (21.5%), poorer (21.6%), and poorest (22.0%) quintiles. Conversely, the distribution of women with a normal BMI was relatively similar across wealth categories: poorest (20.8%), poorer (18.3%), middle (21.5%), richer (19.3%), and richest (20.0%). The distribution of participants across regions varied significantly. The highest proportions were seen in Amman, with 39.9% of normal BMI and 37.3% of obese women. In terms of media consumption, 57.7% of women with a normal BMI and 60.4% of obese women did not read newspapers or magazines at all. Among those who did, 21.4% of women with a normal BMI and 19.7% of obese women read newspapers or magazines less than once a week, while 20.9% of women with a normal BMI and 19.9% of obese women read them at least once a week. Similarly, when it came to radio listening, 52.7% of women with a normal BMI and 53.9% of obese women reported not listening at all. Of those who did, 23.3% of women with a normal BMI and 23.1% of obese women listened less than once a week, while 24.0% of women with a normal BMI and 23.0% of obese women listened at least once a week. In terms of television watching, our results showed that most women with a normal BMI (74.4%) and those with obesity (74.9%) watched television at least once a week. Regarding Internet usage over the past month, most women with a normal BMI (69.99%) and those with obesity (61.3%) accessed it almost every day. When it comes to cigarette smoking, most women with a normal BMI (90.3%) and those with obesity (92.9%) are non-smokers. In our multivariate analysis (Table 2 ), a clear trend of increasing obesity odds was observed with advancing age. Compared to the reference group (ages 20–24), the adjusted odds ratios (AOR) for obesity were significantly higher in the older age groups: 30–34 years (AOR: 2.34; 95% CI: 1.61–3.39; p < 0.001), 35–39 years (AOR: 4.70; 95% CI: 3.13–7.06; p < 0.001), 40–44 years (AOR: 8.08; 95% CI: 5.40-12.08; p < 0.001), and 45–49 years (AOR: 11.71; 95% CI: 7.92–17.33; p < 0.001). However, the age group 25–29 years (AOR: 1.11; 95% CI: 0.76–1.63; p < 0.001) did not show a significant association with obesity. Table 2 Predictors of women's obesity Parameter Estimates Variables B Standard Error AOR 95% Confidence Interval for AOR P-value Lower Upper Age in 5-year groups 20–24 Reference 25–29 0.105 0.195 1.110 0.757 1.629 0.592 30–34 0.848 0.190 2.336 1.608 3.393 < 0.001 35–39 1.548 0.207 4.701 3.130 7.061 < 0.001 40–44 2.089 0.205 8.075 5.399 12.078 < 0.001 45–49 2.461 0.199 11.714 7.920 17.326 < 0.001 Highest educational level No education Reference Primary 0.539 0.334 1.714 0.890 3.303 0.107 Secondary 0.336 0.267 1.399 0.829 2.362 0.209 Higher 0.134 0.287 1.143 0.651 2.006 0.641 Type of place of residence Urban Reference Rural 0.054 0.153 1.055 0.782 1.424 0.724 Wealth index combined Poorest Reference Poorer 0.129 0.151 1.138 0.847 1.529 0.392 Middle -0.116 0.158 0.890 0.653 1.214 0.461 Richer -0.225 0.179 0.798 0.562 1.133 0.208 Richest -0.806 0.216 0.447 0.292 0.682 < 0.001 Region Amman Reference Balqa -0.313 0.211 0.731 0.483 1.106 0.138 Zarqua 0.218 0.177 1.243 0.878 1.760 0.220 Madaba 0.245 0.182 1.278 0.894 1.826 0.178 Irbid 0.116 0.172 1.123 0.801 1.573 0.501 Mafraq 0.152 0.180 1.164 0.818 1.656 0.399 Jerash -0.086 0.265 0.917 0.545 1.544 0.745 Aljoun -0.129 0.237 0.879 0.552 1.399 0.585 Karak -0.153 0.222 0.858 0.555 1.326 0.491 Tafilh 0.807 0.208 2.241 1.491 3.369 < 0.001 Maan -0.963 0.233 0.382 0.242 0.602 < 0.001 Aquaba -1.268 0.230 0.281 0.179 0.442 < 0.001 Frequency of reading newspaper or magazine Not at all Reference Less than once a week -0.176 0.149 0.839 0.626 1.123 0.237 At least once a week -0.163 0.161 0.849 0.619 1.164 0.310 Frequency of listening to radio Not at all Reference Less than once a week 0.115 0.136 1.122 0.859 1.467 0.398 At least once a week 0.096 0.160 1.101 0.804 1.508 0.548 Frequency of watching television Not at all Reference Less than once a week 0.132 0.223 1.141 0.737 1.767 0.554 At least once a week 0.152 0.182 1.164 0.815 1.662 0.403 Frequency of using the Internet last month Not at all Reference Less than once a week 0.183 0.318 1.201 0.644 2.240 0.564 At least once a week -0.084 0.181 0.919 0.645 1.310 0.641 Almost every day -0.062 0.140 0.940 0.714 1.237 0.657 Frequency smokes cigarettes do not smoke Reference some days -0.251 0.395 0.778 0.358 1.690 0.525 every day -0.495 0.231 0.609 0.387 0.959 0.032 Abbreviations: AOR = Adjusted odds ratio Educational level did not show a significant relationship with obesity. Women with primary education had an AOR of 1.71 (95% CI: 0.89–3.30; p = 0.107), those with secondary education had an AOR of 1.4 (95% CI: 0.83–2.36; p = 0.21), and higher education was associated with an AOR of 1.14 (95% CI: 0.65–2.01; p = 0.64). Also, Rural residency did not show a significant relationship with obesity (AOR: 1.06; 95% CI: 0.65–2.01; p = 0.64). Wealth quintiles showed varied impacts on obesity risk. Participants in the poorer (OR: 1.04; 95% CI: 0.85–1.27; p = 0.697), middle (OR: 0.86; 95% CI: 0.69–1.07; p = 0.179), and richer (OR: 0.89; 95% CI: 0.69–1.15; p = 0.383) groups had no significant difference in obesity odds compared to the poorest quintile. However, those in the richest quintile had a significantly lower likelihood of obesity (OR: 0.48; 95% CI: 0.34–0.66; p < 0.001). Regional differences in obesity were notable. Tafilh had a significantly higher likelihood of obesity compared to Amman (OR: 2.25; 95% CI: 1.62–3.12; p < 0.001), while Maan (OR: 0.39; 95% CI: 0.27–0.56; p < 0.001) and Aquaba (OR: 0.26; 95% CI: 0.19–0.38; p < 0.001) showed a significant lower odd. In our analysis of smoking and obesity, we found that individuals who smoked every day were significantly less likely to be obese, with an odds ratio of 0.61 (95% confidence interval: 0.39–0.96; p = 0.032). Conversely, individuals who smoked only on some days did not show a significant association with obesity, as indicated by an odds ratio of 0.78 (95% confidence interval: 0.36–1.7; p = 0.525). Our analysis found no significant association between obesity and the frequency of watching television, listening to the radio, reading newspapers or magazines, and using the Internet last month. 4. Discussion Our study is the first to identify obesity risk factors among adult women in Jordan using nationally representative data. The analysis revealed a strong correlation between age and obesity, with older women being significantly more affected. Specifically, women aged 44–49 were 12.71 times more likely to be obese compared to those aged 20–24. These findings align with studies by Al Nsour et al. in Jordan( 18 ), Rawal et al. in Nepal( 21 ), Mbochi et al. in Nairobi( 22 ), and Mukora et al. in Zimbabwe( 23 ), which also reported higher obesity rates among older women. This trend may be attributed to the hormonal changes that occur with aging. As women approach menopause, a decline in estrogen and an increase in circulating androgens lead to significant shifts in body composition. These hormonal changes contribute to muscle loss, increased abdominal fat, and alterations in body shape. When combined with a sedentary lifestyle, these factors reduce overall energy expenditure and basal metabolic rate, further elevating the risk of obesity in this demographic( 24 , 25 ). Moreover, as women age, a decline in physical function often occurs, partly due to significant changes in body composition, such as sarcopenia—the age-related reduction in skeletal muscle mass. This decline in muscle mass can lead to slower gait speed, impaired balance and coordination, reduced bone mineral density, and a diminished quality of life. Physical activity plays a crucial role in counteracting these physiological declines associated with aging( 26 ). Therefore, maintaining adequate physical activity levels can enhance longevity and reduce the risk of metabolic and other chronic diseases. The analysis of the wealth index and its association with obesity among women yielded noteworthy findings. Women categorized as "Richest" were significantly less likely to be obese compared to those in the "Poorest" category. However, no statistically significant differences in obesity odds were observed among the "Poorer," "Middle," and "Richer" categories when compared to the "Poorest" group. Our results are consistent with the study by Al Nsour et al( 18 ). conducted in Jordan. However, they contrast with findings from Rawal et al. in Nepal ( 1 ), Mukora et al. in Zimbabwe ( 3 ), and El-Qushayri et al. in Egypt ( 9 ), which reported a higher risk of obesity with increasing wealth. One possible explanation for our results is that extreme wealth may confer protection against obesity, potentially due to better access to healthier food, healthcare, and lifestyle choices. Tara Templin's research suggests that, in high-income countries, obesity is more prevalent among poorer populations, reflecting a shift in obesity patterns as countries develop( 27 ). While higher wealth often improves dietary choices and health outcomes, it can also lead to weight gain due to lifestyle changes associated with increased affluence, particularly among women( 28 , 29 ). This underscores the need for public health strategies that account for varying socioeconomic dynamics to effectively address obesity( 27 , 29 ). The analysis of obesity odds across different regions reveals significant variations. In the Central and Northern regions, there were no notable differences in obesity odds. However, In the Southern region, there was variability in obesity rates between different areas. Specifically, living in Tafilhwas associated with an increased likelihood of obesity, whereas residing in Maan and Aqaba was linked to a decreased likelihood of obesity. This finding aligns with the study by Al Nsour et al. conducted in Jordan in 2009 which reported a significant increase in obesity odds across both northern and southern regions of Jordan, without specifying the areas associated with this risk( 18 ). These regional differences may be attributed to socioeconomic factors, with the Central regions generally having a higher socioeconomic status compared to the Northern and Southern regions (16,30). Variations in obesity prevalence by region may reflect the impact of education and socioeconomic status on health. Higher educational attainment and socioeconomic status are often associated with greater health awareness and more positive attitudes toward healthy lifestyles, in contrast to those with lower levels of education and socioeconomic status(31). We recommend conducting further research to gain a deeper understanding of the variations in obesity prevalence across different regions. The analysis of smoking frequency reveals an interesting association. Notably, daily smoking among women is significantly associated with lower obesity rates, aligning with previous findings by Al Nsour et al. 2009 ( 18 ), Watanabe et al. 2016(32), and Dare et al. 2015 (33). The relationship between smoking and obesity is complex and influenced by multiple factors. While current smokers tend to have a lower obesity risk—likely due to nicotine’s metabolic effects and appetite suppression—former smokers often experience increased central fat accumulation and higher obesity rates(34). Watanabe et al. observed that the prevalence of obesity often rises with the number of cigarettes smoked daily and cumulative pack-years, though not necessarily with the duration of smoking(32). Thus, while smoking may be linked to reduced obesity risk, it also brings substantial health risks, including higher rates of central adiposity, cardiovascular disease, and cancers(34,35). Importantly, the evidence does not support the belief that smoking protects against weight gain. Highlighting this in educational programs may be an effective strategy for discouraging smoking initiation among youth. 4.1 Strength and limitation The key strength of our study lies in the use of nationally representative data to identify predictors of obesity among Jordanian women of reproductive age. Additionally, the JDHS data collection tool is standardized and validated, minimizing the potential for bias and error compared to smaller studies. However, our study has several limitations that warrant further investigation in future research. Firstly, the cross-sectional design of the study limits our ability to establish causality between obesity and its associated risk factors or complications. Secondly, the survey only included participants up to 49 years old, highlighting the need for future studies to include older age groups. Thirdly, the survey data did not account for comorbidities, which are crucial in the development of obesity. Lastly, since only ever-married women were eligible for the study, we were unable to include marital status as a predictor of obesity. 4.2 Recommendation Based on study findings, several recommendations can be made to address obesity among Jordanian women. Given the strong correlation between age and obesity, targeted interventions should be developed for older women, focusing on promoting healthy lifestyle choices and regular physical activity. Efforts should also be made to bridge the disparity between wealth categories by ensuring that all socioeconomic groups have access to resources that promote healthy weight management. The regional variations in obesity odds, particularly in the Southern region, highlight the importance of region-specific public health strategies that address local cultural and socioeconomic factors. Future research should examine the relationship between smoking and obesity in Jordan, which could be more finely tuned by examining the influences of the number of cigarettes smoked daily, cumulative pack-years, and years of smoking on the prevalence of obesity among women. 5. conclusion In conclusion, older women and those living in Tafilah are associated with higher obesity rates among Jordanian women. Conversely, wealthier women and those residing in Maan and Aqaba are linked to lower obesity rates. Implementing targeted interventions that focus on these specific demographics and regions could help reduce obesity and its related health risks. Declarations Conflicts of interest: All the authors declare no conflict of interest. Funding: All author(s) received no financial support for the research, authorship, and/or publication of this article. Ethics approval and consent to participate: Not applicable as we obtained the data from a publicly accessible database (https://dhsprogram.com/data/available-datasets.cfm). Consent for publication: not applicable. Availability of data and material: Data is available upon request from ICF International's website (https://dhsprogram.com/data/available-datasets.cfm). Acknowledgment: I would like to thank Dr. Mohamed Abd-ElGawad for his invaluable mentorship and unwavering support throughout my research journey. Authors’ Contributions: Mahmoud Shaaban Abdelgalil contributed to the study by validating the research idea, performing the data analysis, and writing the discussion section. Sara Hosny El-Farargy requested data from the Demographic and Health Survey, performed data cleaning, and drafted the introduction. Mohamed Adel Dowidar contributed by developing the methods section and compiling the results. 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J Obes [Internet]. 2011 [cited 2024 Aug 28];2011. https://pubmed.ncbi.nlm.nih.gov/21941635/ WHO EMRO | Overweight and obesity among Jordanian women. and their social determinants | Volume 19, issue 12 | EMHJ volume 19, 2013 [Internet]. [cited 2024 Aug 21]. https://www.emro.who.int/emhj-vol-19-2013/12/overweight-and-obesity-among-jordanian-women-and-their-social-determinants.html Adult BMI. Calculator | Healthy Weight, Nutrition, and Physical Activity | CDC [Internet]. [cited 2024 Aug 27]. https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/english_bmi_calculator/bmi_calculator.html The DHS Program. - Using Datasets for Analysis [Internet]. [cited 2024 Oct 31]. https://dhsprogram.com/data/Using-Datasets-for-Analysis.cfm Rawal IDLB, Kanda K, Alam Mahumud R, Joshi DI, Mehata S, Shrestha NI et al. Prevalence of underweight, overweight and obesity and their associated risk factors in Nepalese adults: Data from a Nationwide Survey, 2016. 2018 [cited 2024 Aug 20]; https://doi.org/10.1371/journal.pone.0205912 Mbochi RW, Kuria E, Kimiywe J, Ochola S, Steyn NP. Predictors of overweight and obesity in adult women in Nairobi Province, Kenya. 2012 [cited 2024 Aug 20]; http://www.biomedcentral.com/1471-2458/12/823 Mukora-Mutseyekwa F, Zeeb H, Nengomasha L, Adjei NK. Trends in Prevalence and Related Risk Factors of Overweight and Obesity among Women of Reproductive Age in Zimbabwe, 2005–2015. Int J Environ Res Public Health [Internet]. 2019 Aug 1 [cited 2024 Aug 20];16(15). /pmc/articles/PMC6695964/ Ko SH, Jung Y. Energy Metabolism Changes and Dysregulated Lipid Metabolism in Postmenopausal Women. Nutrients [Internet]. 2021 Dec 1 [cited 2024 Aug 13];13(12). https://doi.org/10.3390/nu13124556 Pascot A, Lemieux S, Lemieux I, Prud’homme D, Tremblay A, Bouchard C et al. Age-related increase in visceral adipose tissue and body fat and the metabolic risk profile of premenopausal women. Diabetes Care [Internet]. 1999 Sep [cited 2024 Aug 13];22 9(9):1471–8. https://doi.org/10.2337/DIACARE.22.9.1471 Kendall KL, Fairman CM. Women and exercise in aging. J Sport Health Sci [Internet]. 2014 [cited 2024 Aug 13];3(3):170–8. https://doi.org/10.1016/J.JSHS.2014.02.001 Templin T, Hashiguchi TCO, Thomson B, Dieleman J, Bendavid E. The overweight and obesity transition from the wealthy to the poor in low- and middle-income countries: A survey of household data from 103 countries. PLoS Med [Internet]. 2019 Nov 27 [cited 2024 Aug 20];16(11):1–15. https://typeset.io/papers/the-overweight-and-obesity-transition-from-the-wealthy-to-4d4hlq2ydk (11) (PDF) A structural model of wealth, obesity and health in the UK [Internet]. [cited 2024 Aug 20]. https://www.researchgate.net/publication/23509758_A_structural_model_of_wealth_obesity_and_health_in_the_UK Au N, Johnston DW. Too Much of a Good Thing? Exploring the Impact of Wealth on Weight. Health Economics (United Kingdom) [Internet]. 2015 Nov 1 [cited 2024 Aug 20];24(11):1403–21. https://typeset.io/papers/too-much-of-a-good-thing-exploring-the-impact-of-wealth-on-4ki84xz6yz Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Jan, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 01 Dec, 2024 Reviews received at journal 29 Nov, 2024 Reviews received at journal 29 Nov, 2024 Reviews received at journal 24 Nov, 2024 Reviewers agreed at journal 23 Nov, 2024 Reviews received at journal 23 Nov, 2024 Reviewers agreed at journal 22 Nov, 2024 Reviewers agreed at journal 21 Nov, 2024 Reviewers agreed at journal 20 Nov, 2024 Reviewers invited by journal 20 Nov, 2024 Editor invited by journal 12 Nov, 2024 Editor assigned by journal 12 Nov, 2024 Submission checks completed at journal 11 Nov, 2024 First submitted to journal 06 Nov, 2024 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. 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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-5402078","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":382499908,"identity":"f7a5a672-f26a-4e3d-8572-9cb7fbc4aa0f","order_by":0,"name":"Mahmoud Shaaban Abdelgalil","email":"data:image/png;base64,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","orcid":"","institution":"Ain-shams University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Mahmoud","middleName":"Shaaban","lastName":"Abdelgalil","suffix":""},{"id":382499909,"identity":"aa75583d-5d87-46dd-b496-1b67ba049206","order_by":1,"name":"Sara Hosny El-Farargy","email":"","orcid":"","institution":"Benha University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sara","middleName":"Hosny","lastName":"El-Farargy","suffix":""},{"id":382499913,"identity":"faf20e23-2e1c-4aa5-aaba-27a999011a4b","order_by":2,"name":"Mohamed Adel Dowidar","email":"","orcid":"","institution":"Sultan Qaboos University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Adel","lastName":"Dowidar","suffix":""},{"id":382499915,"identity":"f9bc2534-cc84-4624-9957-9480429c9f5f","order_by":3,"name":"Mohamed Abd-ElGawad","email":"","orcid":"","institution":"Fayoum University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Abd-ElGawad","suffix":""}],"badges":[],"createdAt":"2024-11-06 10:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5402078/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5402078/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-21431-1","type":"published","date":"2025-01-30T15:58:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75351535,"identity":"a0030195-7efa-4d63-abff-78f35b565c6b","added_by":"auto","created_at":"2025-02-03 16:12:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1548648,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5402078/v1/26de4250-854b-4389-9c82-cb0a071f041f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Socio-Demographic Determinants of Obesity in Jordanian Women of Reproductive Age: Insights from a Nationwide Survey","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eObesity has become a critical public health challenge globally, particularly in low- and middle-income countries undergoing rapid socioeconomic transitions(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The World Health Organization defines obesity as an excessive fat accumulation that poses significant health risks (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Over recent decades, global obesity rates have escalated dramatically, with the Middle East, including Jordan, experiencing a notable increase(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrent estimates indicate that approximately 2\u0026nbsp;billion adults worldwide are overweight, with 677.6\u0026nbsp;million classified as obese (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Women represent a significant proportion of this group, comprising 393.5\u0026nbsp;million of the global obese population (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Obesity in women is associated with numerous adverse health outcomes, including increased risks of cardiovascular diseases, type 2 diabetes, and various malignancies, such as breast, ovarian, and endometrial cancers(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). These conditions contribute to premature mortality, reduced productivity, economic burdens on healthcare systems, and psychological distress(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The COVID-19 pandemic further underscored the vulnerability of obese individuals, demonstrating an elevated risk of severe outcomes, including hospitalization and mortality. A recent meta-analysis of 75 international studies found that obese adults face a 113% higher risk of hospitalization, a 74% higher risk of ICU admission, and a 48% increased risk of death compared to those with normal weight (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This escalating obesity crisis imposes a significant strain on healthcare systems and carries a global economic burden of approximately \u003cspan\u003e$\u003c/span\u003e2 trillion annually(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Additionally, maternal obesity is associated with a higher likelihood of obesity in offspring, perpetuating an intergenerational cycle of obesity(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile overweight and obesity were once considered problems exclusive to high-income countries, they have become global issues, impacting low- and middle-income nations as well, including those in the Eastern Mediterranean Region (EMR) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Within the EMR, which includes Jordan, obesity rates are alarmingly high, significantly contributing to the burden of non-communicable diseases. In Jordan, the rising prevalence of obesity among adults presents a significant challenge to the healthcare system (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Obesity rates are notably higher among females, at 43.1%, compared to 28.2% among males(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Over the past two decades, this prevalence has increased at an average annual rate of 1.38% (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Key factors contributing to this trend include dietary shifts, sedentary lifestyles, urbanization, marital status, and cultural norms (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMusaiger et al.'s systematic review of obesity in the EMR identified several key factors that contribute to the increased risk of obesity in the region. These factors include dietary shifts, physical inactivity, urbanization, marital status, shorter breastfeeding durations, frequent snacking, skipping breakfast, high consumption of sugary drinks, increased dining out, prolonged television viewing, aggressive marketing of high-fat foods, stunting, body image perceptions, cultural influences, and food subsidy policies(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).Another study using data from the Jordan Population and Family Health Survey (JPFHS) 2009 reported that factors such as age, residence in the southern regions of Jordan, early marriage, parity, wealth status, and smoking are associated with obesity over time(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite existing research, there is a lack of recent evidence on the socioeconomic, behavioral, and reproductive factors linked to obesity. This study aims to investigate the socio-demographic correlates of obesity among adult ever-married women in Jordan by utilizing updated datasets from the JPFHS 2017-18.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Source\u003c/h2\u003e \u003cp\u003eThis study utilized data from the JPFHS conducted in 2017-18. The survey included a nationally representative sample of women aged 15\u0026ndash;49 years across Jordan's 12 governorates. Anthropometric measurements, including body mass index (BMI), were collected to assess the nutritional status of the participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Inclusion Criteria\u003c/h2\u003e \u003cp\u003eThe study focused on adult ever-married women aged 20\u0026ndash;49 years who had complete BMI data. Women with a BMI measured according to the Centers for Disease Control and Prevention guidelines were included(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Exclusion Criteria\u003c/h2\u003e \u003cp\u003eWomen who had missing BMI data, those classified as underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5), overweight (BMI 25\u0026ndash;30), or women aged below 20 years were excluded from the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Included Variables\u003c/h2\u003e \u003cp\u003eIn our cross-sectional study, we analyzed a variety of sociodemographic and behavioral variables to assess their association with obesity among Jordanian women of reproductive age. Age was categorized into six 5-year groups: 20\u0026ndash;24, 25\u0026ndash;29, 30\u0026ndash;34, 35\u0026ndash;39, 40\u0026ndash;44, and 45\u0026ndash;49 years. The type of place of residence was classified as either urban or rural. Educational level was divided into four categories: no education, primary, secondary, and higher. The wealth index was grouped into five categories: poorest, poorer, middle, richer, and richest. Regional analysis was conducted across three major regions: Central (Amman, Balqa, Zarqua, Madaba), Northern (Irbid, Mafraq, Jerash, Aljoun), and Southern (Karak, Tafilh, Maan, Aqaba).\u003c/p\u003e \u003cp\u003eWe also examined the frequency of media consumption, including reading newspapers or magazines, listening to the radio, and watching television, each categorized as not at all, less than once a week, or at least once a week. Household characteristics included whether the household owned a car or truck (yes/no) and the type of cooking fuel used, categorized as electricity, natural gas, kerosene, coal/lignite, or no food cooked in-house. Internet usage over the past month was assessed as not at all, less than once a week, at least once a week, or almost every day. Finally, we evaluated the current contraceptive method used by participants, including options such as not using any method, pill, IUD, injections, male condom, female sterilization, male sterilization, periodic abstinence, withdrawal, implants/Norplant, and lactational amenorrhea (LAM).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.5 Statistical Analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS version 24. We used a weighted count for the analysis based on DHS recommendations(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The sample weight was an eight-digit variable with six implied decimal places, and the weighting factor was divided by 1,000,000 for application. Descriptive statistics were used to summarize the characteristics of the study population, with results reported as frequencies and percentages. Multivariable logistic regression analyses were performed to identify predictors of obesity. The results were presented as adjusted odds ratios (AORs) with 95% confidence intervals (CIs). A p-value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eData were collected from 4,226 Jordanian women of reproductive age (20\u0026ndash;49 years). Of these participants, 2,170 were classified as obese, while 2,056 had a normal BMI \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\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\u003eCharacteristics of the included women according to BMI\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003e(BMI\u0026thinsp;=\u0026thinsp;18.5: 25)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2056\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003cp\u003e(BMI\u0026thinsp;\u0026gt;\u0026thinsp;30)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2170\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003cp\u003eN %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003cp\u003eN %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eAge in 5-year groups\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e20\u0026ndash;24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e343 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e25\u0026ndash;29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e509 (24.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e177 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e30\u0026ndash;34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e445 (21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e312 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e35\u0026ndash;39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e306 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e404 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e40\u0026ndash;44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e257 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e559 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e45\u0026ndash;49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e197 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e603 (27.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eType of place of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUrban\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1931 (89.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1927 (88.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRural\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e223 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e258 (11.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eHighest educational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNo education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePrimary\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSecondary\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1177 (54.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1327 (60.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHigher\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e817 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e619 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eWealth index combined\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePoorest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e448 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e481 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePoorer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e394 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e473 (21.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMiddle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e464 (21.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e470 (21.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRicher\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e416 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e456 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRichest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e432 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e305 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCentral Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e860 (39.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e815 (37.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBalqa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZarqua\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e272 (12.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e348 (15.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMadaba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNorth Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIrbid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e369 (17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e418 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMafraq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e130 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e159 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJerash\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAljoun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSouth Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKarak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTafilh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAquaba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eFrequency of reading newspaper or magazine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNot at all\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1243 (57.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1320 (60.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLess than once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e460 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e430 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAt least once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e451 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e436 (19.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eFrequency of listening to radio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNot at all\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1134 (52.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1179 (53.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLess than once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e502 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e504 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAt least once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e518 (24.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e502 (23.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eFrequency of watching television\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNot at all\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e197 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e190 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLess than once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e355 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e357 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAt least once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1603 (74.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1638 (74.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eFrequency of using the Internet last month\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNot at all\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e425 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e598 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLess than once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAt least once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAlmost every day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1505 (69.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1340 (61.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eFrequency smokes cigarettes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDo not smoke\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1945 (90.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2029 (92.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEvery day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSome days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46 (2.1%)\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\u003eThe analysis revealed that the highest proportion of obese women was in the 40\u0026ndash;49 age group, accounting for 53.6% of the total obese population. In contrast, 46.4% of women with a normal BMI were between 25\u0026ndash;34 age groups. Regarding residence, 88.2% of women with a normal BMI and 89.6% of obese women lived in urban areas. Among women with a normal BMI, over half had completed secondary education, while 5.0% had completed primary education, 37.9% had higher education, and 2.4% had no formal education. In the obese group, 60.8% had secondary education, 8.2% had primary education, 28.3% had higher education, and 2.7% had no formal education.\u003c/p\u003e \u003cp\u003eObesity was least prevalent among women in the richest wealth quintile, affecting 14.0% of women in this group. In comparison, obesity rates were higher in the richer (20.9%), middle (21.5%), poorer (21.6%), and poorest (22.0%) quintiles. Conversely, the distribution of women with a normal BMI was relatively similar across wealth categories: poorest (20.8%), poorer (18.3%), middle (21.5%), richer (19.3%), and richest (20.0%). The distribution of participants across regions varied significantly. The highest proportions were seen in Amman, with 39.9% of normal BMI and 37.3% of obese women.\u003c/p\u003e \u003cp\u003eIn terms of media consumption, 57.7% of women with a normal BMI and 60.4% of obese women did not read newspapers or magazines at all. Among those who did, 21.4% of women with a normal BMI and 19.7% of obese women read newspapers or magazines less than once a week, while 20.9% of women with a normal BMI and 19.9% of obese women read them at least once a week.\u003c/p\u003e \u003cp\u003eSimilarly, when it came to radio listening, 52.7% of women with a normal BMI and 53.9% of obese women reported not listening at all. Of those who did, 23.3% of women with a normal BMI and 23.1% of obese women listened less than once a week, while 24.0% of women with a normal BMI and 23.0% of obese women listened at least once a week.\u003c/p\u003e \u003cp\u003eIn terms of television watching, our results showed that most women with a normal BMI (74.4%) and those with obesity (74.9%) watched television at least once a week. Regarding Internet usage over the past month, most women with a normal BMI (69.99%) and those with obesity (61.3%) accessed it almost every day. When it comes to cigarette smoking, most women with a normal BMI (90.3%) and those with obesity (92.9%) are non-smokers.\u003c/p\u003e \u003cp\u003eIn our multivariate analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), a clear trend of increasing obesity odds was observed with advancing age. Compared to the reference group (ages 20\u0026ndash;24), the adjusted odds ratios (AOR) for obesity were significantly higher in the older age groups: 30\u0026ndash;34 years (AOR: 2.34; 95% CI: 1.61\u0026ndash;3.39; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 35\u0026ndash;39 years (AOR: 4.70; 95% CI: 3.13\u0026ndash;7.06; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 40\u0026ndash;44 years (AOR: 8.08; 95% CI: 5.40-12.08; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 45\u0026ndash;49 years (AOR: 11.71; 95% CI: 7.92\u0026ndash;17.33; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the age group 25\u0026ndash;29 years (AOR: 1.11; 95% CI: 0.76\u0026ndash;1.63; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) did not show a significant association with obesity.\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\u003ePredictors of women's obesity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eParameter Estimates\u003c/p\u003e \u003c/th\u003e \u003c/tr\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\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e95% Confidence Interval for AOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eAge in 5-year groups\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e20\u0026ndash;24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\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 \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e25\u0026ndash;29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e30\u0026ndash;34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e35\u0026ndash;39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e40\u0026ndash;44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e45\u0026ndash;49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eHighest educational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNo education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\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 \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePrimary\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSecondary\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHigher\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eType of place of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUrban\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\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 \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRural\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eWealth index combined\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePoorest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\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 \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePoorer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMiddle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRicher\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRichest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAmman\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\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 \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBalqa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eZarqua\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMadaba\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIrbid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMafraq\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eJerash\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAljoun\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eKarak\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTafilh\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMaan\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAquaba\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eFrequency of reading newspaper or magazine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNot at all\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\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 \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLess than once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAt least once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eFrequency of listening to radio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNot at all\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\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 \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLess than once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAt least once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eFrequency of watching television\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNot at all\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\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 \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLess than once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAt least once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eFrequency of using the Internet last month\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNot at all\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\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 \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLess than once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAt least once a week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAlmost every day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eFrequency smokes cigarettes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003edo not smoke\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\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 \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003esome days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eevery day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: AOR\u0026thinsp;=\u0026thinsp;Adjusted odds ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEducational level did not show a significant relationship with obesity. Women with primary education had an AOR of 1.71 (95% CI: 0.89\u0026ndash;3.30; p\u0026thinsp;=\u0026thinsp;0.107), those with secondary education had an AOR of 1.4 (95% CI: 0.83\u0026ndash;2.36; p\u0026thinsp;=\u0026thinsp;0.21), and higher education was associated with an AOR of 1.14 (95% CI: 0.65\u0026ndash;2.01; p\u0026thinsp;=\u0026thinsp;0.64). Also, Rural residency did not show a significant relationship with obesity (AOR: 1.06; 95% CI: 0.65\u0026ndash;2.01; p\u0026thinsp;=\u0026thinsp;0.64).\u003c/p\u003e \u003cp\u003eWealth quintiles showed varied impacts on obesity risk. Participants in the poorer (OR: 1.04; 95% CI: 0.85\u0026ndash;1.27; p\u0026thinsp;=\u0026thinsp;0.697), middle (OR: 0.86; 95% CI: 0.69\u0026ndash;1.07; p\u0026thinsp;=\u0026thinsp;0.179), and richer (OR: 0.89; 95% CI: 0.69\u0026ndash;1.15; p\u0026thinsp;=\u0026thinsp;0.383) groups had no significant difference in obesity odds compared to the poorest quintile. However, those in the richest quintile had a significantly lower likelihood of obesity (OR: 0.48; 95% CI: 0.34\u0026ndash;0.66; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eRegional differences in obesity were notable. Tafilh had a significantly higher likelihood of obesity compared to Amman (OR: 2.25; 95% CI: 1.62\u0026ndash;3.12; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while Maan (OR: 0.39; 95% CI: 0.27\u0026ndash;0.56; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Aquaba (OR: 0.26; 95% CI: 0.19\u0026ndash;0.38; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed a significant lower odd.\u003c/p\u003e \u003cp\u003eIn our analysis of smoking and obesity, we found that individuals who smoked every day were significantly less likely to be obese, with an odds ratio of 0.61 (95% confidence interval: 0.39\u0026ndash;0.96; p\u0026thinsp;=\u0026thinsp;0.032). Conversely, individuals who smoked only on some days did not show a significant association with obesity, as indicated by an odds ratio of 0.78 (95% confidence interval: 0.36\u0026ndash;1.7; p\u0026thinsp;=\u0026thinsp;0.525).\u003c/p\u003e \u003cp\u003eOur analysis found no significant association between obesity and the frequency of watching television, listening to the radio, reading newspapers or magazines, and using the Internet last month.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study is the first to identify obesity risk factors among adult women in Jordan using nationally representative data. The analysis revealed a strong correlation between age and obesity, with older women being significantly more affected. Specifically, women aged 44\u0026ndash;49 were 12.71 times more likely to be obese compared to those aged 20\u0026ndash;24. These findings align with studies by Al Nsour et al. in Jordan(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), Rawal et al. in Nepal(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), Mbochi et al. in Nairobi(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), and Mukora et al. in Zimbabwe(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), which also reported higher obesity rates among older women. This trend may be attributed to the hormonal changes that occur with aging. As women approach menopause, a decline in estrogen and an increase in circulating androgens lead to significant shifts in body composition. These hormonal changes contribute to muscle loss, increased abdominal fat, and alterations in body shape. When combined with a sedentary lifestyle, these factors reduce overall energy expenditure and basal metabolic rate, further elevating the risk of obesity in this demographic(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, as women age, a decline in physical function often occurs, partly due to significant changes in body composition, such as sarcopenia\u0026mdash;the age-related reduction in skeletal muscle mass. This decline in muscle mass can lead to slower gait speed, impaired balance and coordination, reduced bone mineral density, and a diminished quality of life. Physical activity plays a crucial role in counteracting these physiological declines associated with aging(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Therefore, maintaining adequate physical activity levels can enhance longevity and reduce the risk of metabolic and other chronic diseases.\u003c/p\u003e \u003cp\u003eThe analysis of the wealth index and its association with obesity among women yielded noteworthy findings. Women categorized as \"Richest\" were significantly less likely to be obese compared to those in the \"Poorest\" category. However, no statistically significant differences in obesity odds were observed among the \"Poorer,\" \"Middle,\" and \"Richer\" categories when compared to the \"Poorest\" group. Our results are consistent with the study by Al Nsour et al(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). conducted in Jordan. However, they contrast with findings from Rawal et al. in Nepal (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), Mukora et al. in Zimbabwe (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), and El-Qushayri et al. in Egypt (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), which reported a higher risk of obesity with increasing wealth. One possible explanation for our results is that extreme wealth may confer protection against obesity, potentially due to better access to healthier food, healthcare, and lifestyle choices. Tara Templin's research suggests that, in high-income countries, obesity is more prevalent among poorer populations, reflecting a shift in obesity patterns as countries develop(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). While higher wealth often improves dietary choices and health outcomes, it can also lead to weight gain due to lifestyle changes associated with increased affluence, particularly among women(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). This underscores the need for public health strategies that account for varying socioeconomic dynamics to effectively address obesity(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe analysis of obesity odds across different regions reveals significant variations. In the Central and Northern regions, there were no notable differences in obesity odds. However, In the Southern region, there was variability in obesity rates between different areas. Specifically, living in Tafilhwas associated with an increased likelihood of obesity, whereas residing in Maan and Aqaba was linked to a decreased likelihood of obesity. This finding aligns with the study by Al Nsour et al. conducted in Jordan in 2009 which reported a significant increase in obesity odds across both northern and southern regions of Jordan, without specifying the areas associated with this risk(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese regional differences may be attributed to socioeconomic factors, with the Central regions generally having a higher socioeconomic status compared to the Northern and Southern regions (16,30). Variations in obesity prevalence by region may reflect the impact of education and socioeconomic status on health. Higher educational attainment and socioeconomic status are often associated with greater health awareness and more positive attitudes toward healthy lifestyles, in contrast to those with lower levels of education and socioeconomic status(31). We recommend conducting further research to gain a deeper understanding of the variations in obesity prevalence across different regions.\u003c/p\u003e \u003cp\u003eThe analysis of smoking frequency reveals an interesting association. Notably, daily smoking among women is significantly associated with lower obesity rates, aligning with previous findings by Al Nsour et al. 2009 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), Watanabe et al. 2016(32), and Dare et al. 2015 (33). The relationship between smoking and obesity is complex and influenced by multiple factors. While current smokers tend to have a lower obesity risk\u0026mdash;likely due to nicotine\u0026rsquo;s metabolic effects and appetite suppression\u0026mdash;former smokers often experience increased central fat accumulation and higher obesity rates(34).\u003c/p\u003e \u003cp\u003eWatanabe et al. observed that the prevalence of obesity often rises with the number of cigarettes smoked daily and cumulative pack-years, though not necessarily with the duration of smoking(32). Thus, while smoking may be linked to reduced obesity risk, it also brings substantial health risks, including higher rates of central adiposity, cardiovascular disease, and cancers(34,35).\u003c/p\u003e \u003cp\u003eImportantly, the evidence does not support the belief that smoking protects against weight gain. Highlighting this in educational programs may be an effective strategy for discouraging smoking initiation among youth.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Strength and limitation\u003c/h2\u003e \u003cp\u003eThe key strength of our study lies in the use of nationally representative data to identify predictors of obesity among Jordanian women of reproductive age. Additionally, the JDHS data collection tool is standardized and validated, minimizing the potential for bias and error compared to smaller studies.\u003c/p\u003e \u003cp\u003eHowever, our study has several limitations that warrant further investigation in future research. Firstly, the cross-sectional design of the study limits our ability to establish causality between obesity and its associated risk factors or complications. Secondly, the survey only included participants up to 49 years old, highlighting the need for future studies to include older age groups. Thirdly, the survey data did not account for comorbidities, which are crucial in the development of obesity. Lastly, since only ever-married women were eligible for the study, we were unable to include marital status as a predictor of obesity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Recommendation\u003c/h2\u003e \u003cp\u003eBased on study findings, several recommendations can be made to address obesity among Jordanian women. Given the strong correlation between age and obesity, targeted interventions should be developed for older women, focusing on promoting healthy lifestyle choices and regular physical activity. Efforts should also be made to bridge the disparity between wealth categories by ensuring that all socioeconomic groups have access to resources that promote healthy weight management. The regional variations in obesity odds, particularly in the Southern region, highlight the importance of region-specific public health strategies that address local cultural and socioeconomic factors. Future research should examine the relationship between smoking and obesity in Jordan, which could be more finely tuned by examining the influences of the number of cigarettes smoked daily, cumulative pack-years, and years of smoking on the prevalence of obesity among women.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. conclusion","content":"\u003cp\u003eIn conclusion, older women and those living in Tafilah are associated with higher obesity rates among Jordanian women. Conversely, wealthier women and those residing in Maan and Aqaba are linked to lower obesity rates. Implementing targeted interventions that focus on these specific demographics and regions could help reduce obesity and its related health risks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u003c/strong\u003e All the authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e All author(s) received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Not applicable as we obtained the data from a publicly accessible database (https://dhsprogram.com/data/available-datasets.cfm).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u003c/strong\u003e Data is available upon request from ICF International\u0026apos;s website (https://dhsprogram.com/data/available-datasets.cfm).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u003c/strong\u003e I would like to thank Dr. Mohamed Abd-ElGawad for his invaluable mentorship and unwavering support throughout my research journey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMahmoud Shaaban Abdelgalil contributed to the study by validating the research idea, performing the data analysis, and writing the discussion section. Sara Hosny El-Farargy requested data from the Demographic and Health Survey, performed data cleaning, and drafted the introduction. Mohamed Adel Dowidar contributed by developing the methods section and compiling the results. Mohamed Abd-ElGawad supervised the project, providing guidance and oversight throughout the research process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePopkin BM, Ng SW. The nutrition transition to a stage of high obesity and noncommunicable disease prevalence dominated by ultra-processed foods is not inevitable. 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[cited 2024 Aug 20]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net/publication/23509758_A_structural_model_of_wealth_obesity_and_health_in_the_UK\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net/publication/23509758_A_structural_model_of_wealth_obesity_and_health_in_the_UK\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAu N, Johnston DW. Too Much of a Good Thing? Exploring the Impact of Wealth on Weight. Health Economics (United Kingdom) [Internet]. 2015 Nov 1 [cited 2024 Aug 20];24(11):1403\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://typeset.io/papers/too-much-of-a-good-thing-exploring-the-impact-of-wealth-on-4ki84xz6yz\u003c/span\u003e\u003cspan address=\"https://typeset.io/papers/too-much-of-a-good-thing-exploring-the-impact-of-wealth-on-4ki84xz6yz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\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":true,"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":"Obesity, Women, Jordan, DHS, JPFHS","lastPublishedDoi":"10.21203/rs.3.rs-5402078/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5402078/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAim: \u003c/strong\u003eWe aimed to explore the predictors associated with obesity among adult ever-married Jordanian women aged 20–49 years based on the Jordanian Population and Family Health Survey (JPFHS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eOur study analyzed data from the JPFHS conducted in 2017-18, which initially included 14,689 ever-married women. We performed both univariate and multivariable analyses to determine the socio-demographic predictors of obesity among these women.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult: \u003c/strong\u003eWe included 4,226 Jordanian women in our study, of whom 2,170 were classified as obese and 2,056 had a normal body mass index (BMI). Multivariable analysis indicated that increasing age and living in Tafilh were significantly associated with higher odds of developing obesity (p \u0026lt; 0.05). Conversely, factors such as being in the wealthiest category, residing in Maan and Aqaba, and smoking every day were significantly linked to reduced odds of obesity (p \u0026lt; 0.05). Additionally, no significant associations were found between obesity development and variables such as the type of place of residence, educational level, frequency of reading newspapers or magazines, radio listening, television watching, or internet use in the past month (p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eAppropriate and targeted interventions should be developed for Jordanian women to address obesity and its related health issues. Policymakers should adopt a multilevel approach that focuses on high-risk subgroups, including older women, and those living in Tafilh. Efforts should be made to raise awareness and provide preventative measures tailored to these groups to effectively reduce obesity and its associated complications.\u003c/p\u003e","manuscriptTitle":"Exploring Socio-Demographic Determinants of Obesity in Jordanian Women of Reproductive Age: Insights from a Nationwide Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 11:25:04","doi":"10.21203/rs.3.rs-5402078/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-02T04:43:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-29T11:20:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-29T08:27:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-24T18:26:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212703354390033583436936070672518843951","date":"2024-11-23T10:28:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-23T09:05:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11457450044473747467369289693602189325","date":"2024-11-22T20:44:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273806288936437035602302710763875595321","date":"2024-11-21T08:09:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122580790988096108687336386852375801816","date":"2024-11-20T18:03:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-20T17:44:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-12T08:47:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-12T08:38:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-11T07:10:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-11-06T10:48:35+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":"0cb47376-8b98-4087-9f9c-e8afea8f9db9","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T16:07:00+00:00","versionOfRecord":{"articleIdentity":"rs-5402078","link":"https://doi.org/10.1186/s12889-025-21431-1","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-01-30 15:58:11","publishedOnDateReadable":"January 30th, 2025"},"versionCreatedAt":"2024-12-02 11:25:04","video":"","vorDoi":"10.1186/s12889-025-21431-1","vorDoiUrl":"https://doi.org/10.1186/s12889-025-21431-1","workflowStages":[]},"version":"v1","identity":"rs-5402078","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5402078","identity":"rs-5402078","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}