Depression and Anxiety among Adults in Kuwait: A Cross-Sectional Study on the Role of Air Pollution

Depression and Anxiety among Adults in Kuwait: A Cross-Sectional Study on the Role of Air Pollution | 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 Depression and Anxiety among Adults in Kuwait: A Cross-Sectional Study on the Role of Air Pollution Anfal AlDaihani, Eiman Alawadhi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6465076/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The increasing global prevalence of mental health problems such as depression and anxiety has inspired further research into environmental risk factors, including air pollution. Particulate matter (PM), primarily PM2.5 and PM10, has been related to neuroinflammation, hormone changes, and changes in brain structure, all of which may have an effect on mental health. However, data from the Middle East are still sparse. Aim This study explored the association between exposure to PM2.5 and PM10 and the prevalence of depression and anxiety among adults living in six urban areas in Kuwait. Methods A cross-sectional study was carried out to evaluate mental health using the DASS-21, a validated online questionnaire. PM concentrations were collected during a 22-month period (January 2022 to October 2023). Descriptive statistics, chi-square tests, and multivariable logistic regression were used to investigate relationships after adjusting for demographic and lifestyle characteristics. Results Among the 640 individuals, 82.8% reported depression and 87.3% anxiety. The risk of depression was considerably raised by exposure to harmful levels of PM2.5 and PM10 (OR = 1.7 and 2.9, respectively). The odds of depression were higher for men (OR = 2.8) and married people (OR = 2.2). Living alone or with others raised the risk of anxiety (OR = 3.3) and depression (OR = 3.1). The risk for both outcomes was doubled by stressful life events. Self-employed or part-time workers (OR = 5.3) and smokers (OR = 4.5) were more likely to experience anxiety. Conclusion This study focuses on the mental health concerns posed by air pollution in Kuwait. It is crucial to address air quality through public health integration, urban planning, and environmental policy. To demonstrate causation and targeted interventions, longitudinal research is needed. Air pollution Particulate matter (PM2.5 and PM10) Depression Anxiety Kuwait Introduction Mental health disorders account for 125 million disability-adjusted life-years (DALYs) globally, ranking seventh among the most significant contributors to global disability in 2019. The co-occurrence of two or more mental health conditions may significantly reduce life expectancy by exacerbating age-related illnesses ( 1 ). With the rising prevalence of depressive and anxiety disorders, there is an urgent need for public health efforts to focus on prevention and to identify modifiable environmental risk factors associated with these conditions ( 2 ). The increasing burden of these disorders, coupled with ambiguity around their causes, has intensified interest in investigating environmental exposures as potential contributors ( 3 ). Among these, anxiety and depression have gained attention for their possible links to air pollution ( 4 ). Ambient air pollution, a recognized environmental health risk since the 1990s ( 5 ), is largely attributed to factors such as urbanization, industrial activity, and widespread reliance on internal combustion engine vehicles—all of which have substantial implications for public health ( 6 , 7 ). There is growing evidence that air pollutants may influence mental health through toxic effects on the central nervous system ( 8 ). Findings from epidemiological and animal studies suggest that exposure to polluted air can lead to neurodevelopmental and neuropsychiatric symptoms ( 9 ). In humans, such exposure may affect areas of the brain responsible for emotional regulation, potentially increasing the risk of depression, anxiety, eating disorders, and addictive behaviors ( 10 – 12 ). Indirect consequences of air pollution may also include decreased labor productivity, lower income, and heightened job insecurity—factors associated with poor mental health outcomes ( 13 ). Other indirect effects, such as increased irritability, reduced outdoor activity, and a general decline in well-being, have also been reported ( 14 ). Particulate matter (PM), a complex mixture of airborne particles, is a major component of air pollution and has been increasingly implicated in both physical and mental health effects ( 7 , 15 ). It has been proposed that neuroinflammation, involving inflammatory responses in the central nervous system (CNS), plays an important role in the development of depression (Y. Liu et al., 2012). In addition, disruption of the hypothalamic–pituitary–adrenal (HPA) axis has been linked to depressive symptoms ( 16 ). PM exposure has been associated with elevated cortisol levels, oxidative stress, glial cell activation, and structural changes in the brain—all of which support a biological basis for its effects on mental health ( 17 , 18 ). In Kuwait, environmental conditions and human activities contribute to elevated levels of air pollution. PM2.5 concentrations in Kuwait City have been reported at levels four times higher than the U.S. National Ambient Air Quality Standards’ recommended annual limit ( 19 ). Similarly, PM10 concentrations have been found to be high due to extreme weather patterns, including elevated temperatures and wind speeds ( 20 ). Given Kuwait’s hot desert climate and its petroleum-driven economy, high pollution levels are to be expected ( 21 , 22 ). The major contributors to air pollution in Kuwait include emissions from vehicles, burning of waste, and activities related to the extraction, storage, and transportation of fossil fuels. These processes, particularly those involving heavy machinery and transport vehicles, are known sources of PM emissions ( 23 ). This study investigates the association between exposure to PM2.5 and PM10 and the prevalence of depression and anxiety among adults living in six urban areas in Kuwait. Data on air pollution were obtained from the Kuwait Environmental Public Authority (EPA), while information on mental health outcomes and related predictors was collected via a structured online questionnaire. Methods This study aimed to examine the association between long-term exposure to PM2.5 and PM10 and symptoms of depression and anxiety among adults in Kuwait. From January 2022 to October 2023, the Kuwait Environmental Public Authority (EPA) monitored levels of particulate matter (PM) across 12 regions in the country. Six areas—Al-Ahmadi, Al-Fahaheel, Al-Jahra, Al-Salam, Saad Al-Abdullah, and Al-Zahra—were randomly selected for analysis. We conducted a cross-sectional study to assess the prevalence of depression and anxiety among adults residing in these selected areas. A validated and reliable self-administered online questionnaire was used to collect the data. Participation was voluntary and open to all adult residents, including both men and women, aged 18 to 65 years. The main exposures in this study were the concentrations of PM2.5 and PM10. These data were obtained from daily EPA measurements taken over a 22-month period in the six selected areas. Average concentrations were calculated for each location. Additionally, the Air Quality Index (AQI) was used to categorize the pollution levels. AQI values between 0 and 100 were classified as “moderate,” while values from 101 to 200 were classified as “unhealthy.” The “unhealthy” category included both levels considered harmful to sensitive groups and to the general population. The online questionnaire was distributed to eligible adults living in the six selected areas. To reach the target population, we employed a combination of convenience and snowball sampling techniques. The survey link was made available at local supermarkets and other public spaces, and participants were encouraged to share it within their communities. Depression and anxiety were measured using the 21-item Depression, Anxiety, and Stress Scale (DASS-21). Each subscale comprised seven items, and responses were rated on a four-point Likert scale ranging from 0 (did not apply at all) to 3 (applied most of the time). The subscale scores were doubled prior to analysis. For depression, a total score above 10 indicated symptoms of depression. For anxiety, a score of 8 or higher was used to identify the presence of anxiety symptoms, based on established cutoff values ( 24 ). The questionnaire also included demographic, socioeconomic, and lifestyle-related variables that could act as potential confounders. These included age, residential area, gender, nationality, education level, employment status, income, marital status, smoking status, and living arrangement. Participants were also asked about stressful life events. Additionally, they provided self-reported weight (in kilograms) and height (in centimetres), from which the body mass index (BMI) was calculated. BMI was classified as underweight (< 18.5), normal (18.5–24.9), overweight (25–29.9), or obese (≥ 30), following CDC guidelines (CDC, 2023). Physical activity was assessed by asking how many days per week participants engaged in moderate to vigorous exercise. Those reporting five or more days per week were considered sufficiently active ( 25 ). Data analysis According to the literature, the mean odds ratio (OR) for depression and anxiety associated with outdoor air pollution exposure was approximately 2.8 ( 26 ). Accordingly, the minimum sample size was calculated to be 310, achieving a power of 80% and a significance level of 0.05, calculated using STATA 15. However, to enhance the accuracy of representing the population and to yield more robust results that account for the use of non-probability sampling, we have decided to double our sample size to 620. The primary outcome variables were depression (yes/no) and anxiety (yes/no), based on the DASS-21 thresholds. The main independent variables were PM2.5 and PM10 AQI categories (moderate or unhealthy). Additional independent variables included demographic, socioeconomic, and lifestyle factors. All analyses were conducted using STATA software version 18. Descriptive statistics were presented as frequencies and percentages. Chi-square tests were used to examine associations between categorical independent and dependent variables. Univariate logistic regression models were used to assess the individual associations of each predictor with depression and anxiety. Predictors with a significance level below 0.1 in univariate analysis were included in the multivariable logistic regression models. Separate multivariable models were estimated to examine predictors of depression and anxiety, respectively. A backward elimination approach was used to identify the final set of predictors in each model. Statistical significance was set at p < 0.05. To identify confounding variables, changes in beta coefficients were assessed before and after inclusion in the model. Variables that altered the coefficient by more than 10% were classified as confounders. Model fit was assessed using the Goodness-of-Fit test, the Likelihood Ratio Test (LRT), and the Akaike Information Criterion (AIC). Multicollinearity among predictors was also evaluated in the final models. Results Using mean concentrations to calculate the Air Quality Index (AQI) for PM2.5, we found that Al-Jahra and Al-Zahra exhibited moderate levels. In contrast, Al-Salam—located near Al-Zahra—and Saad Al-Abdullah—adjacent to Al-Jahra—both had unhealthy PM2.5 levels. Similarly, the adjacent areas of Al-Ahmadi and Al-Fahaheel also showed unhealthy PM2.5 concentrations (Table 1 ). For PM10, the AQI indicated that Al-Zahra and Al-Salam had moderate levels, while Al-Ahmadi recorded an unhealthy PM10 level. Despite being in close proximity, Al-Fahaheel also had an unhealthy PM10 level. Likewise, the neighboring regions of Saad Al-Abdullah and Al-Jahra reported unhealthy PM10 concentrations (Table 1 ). Table 1 The Air Quality Index (AQI) level for PM2.5 and PM10 by the residential area from January 2022 to October 2023. Residential areas PM2.5 PM10 AQI (level of concern) AQI (level of concern) Al-Ahmadi 130 Unhealthy 82 Moderate Al-Fahaheel 118 Unhealthy 106 Unhealthy Al-Jahra 90 Moderate 105 Unhealthy Al-Salam 113 Unhealthy 84 Moderate Al-Zahra 83 Moderate 66 Moderate Saad Al-Abdullah 160 Unhealthy 110 Unhealthy Descriptive Summary A total of 640 adults participated in the study (Table 2 ). Of these, 530 participants (82.8%) reported symptoms of depression, and 559 (87.3%) reported symptoms of anxiety. Regarding air quality exposure, 66.4% of the participants lived in areas classified as having unhealthy levels of PM2.5, while 51.4% were exposed to unhealthy PM10 levels. Participants were relatively evenly distributed across the 18–29 (38.9%) and 30–44 (37.8%) age groups, with 23.3% aged 45 years or older. Gender distribution was balanced, with 51.6% female and 48.4% male participants. The majority (80.2%) were Kuwaiti nationals. In terms of educational attainment, 39.9% held a university degree or higher, 31.8% had a diploma, and 28.3% had completed high school or less. For employment status, 39.5% were self-employed or worked part-time, 27.8% were full-time employees, and 32.7% were unemployed, retired, or students. Income distribution showed that 47.2% earned less than 500 KD per month, 42.8% earned between 500–1500 KD, and 10.1% earned over 1500 KD. Most participants were married (62.4%). With respect to health and lifestyle factors, 61.5% were non-smokers or ex-smokers. A normal body mass index (BMI) was reported by 52.2%, while 38.0% were overweight and 9.8% were obese. A large majority (88.2%) reported being physically inactive. Most participants lived with family (85.2%), and more than half (56.0%) reported experiencing stressful life events. All predictor variables were assessed in relation to the prevalence of depression. A significant association was observed between air quality and depression (Table 2 ). Among those exposed to moderate PM2.5 levels, 76.6% reported symptoms of depression, compared to 85.8% of participants exposed to unhealthy PM2.5 levels (p = 0.004). A similar pattern was found for PM10 exposure: 76.4% of individuals exposed to moderate levels reported depression, versus 88.7% of those exposed to unhealthy PM10 levels (p < 0.001). Factors Associated with Depression A significant association was found between air quality and depression. Participants exposed to unhealthy levels of PM2.5 and PM10 reported higher depression rates (85.8% and 88.7%, respectively) compared to those exposed to moderate levels (76.6% and 76.4%, respectively; p-values < 0.01) (Table 2 ). Depression prevalence increased with age, from 74.7% in those aged 18–29 to 90% in participants aged 45 and above. Men reported a notably higher rate of depression than women (91% vs. 75%; p < 0.001). Lower educational attainment was also associated with greater depression prevalence, particularly among those with a high school diploma or less. Employment status and marital status showed clear trends: higher depression rates were observed among part-time workers and married individuals. Smoking was strongly linked to depression, with 91% of smokers affected versus 77.5% of non-smokers or former smokers. Living with people other than family and reporting stressful life events were also significantly associated with higher depression prevalence (p < 0.01). Factors Associated with Anxiety Anxiety prevalence increased with age, reaching 94.0% among participants aged 45 and over, compared to 92.2% in those aged 30–44 and 78.7% in the youngest age group (p < 0.001). A significant gender difference was also observed, with higher anxiety rates reported among men (93.5%) than women (81.4%) (Table 2 ). Employment status was strongly associated with anxiety. Nearly all self-employed or part-time workers reported symptoms (96.4%), followed by 87.6% of full-time employees and 76.4% of those unemployed, retired, or students (p < 0.001). Anxiety was more common among married individuals (92.2%) compared to those who were single, divorced, or widowed (79.1%). Smoking was another significant factor: 96.7% of smokers experienced anxiety versus 81.3% of non-smokers or former smokers (p < 0.001). Those living alone or with non-family members also reported higher anxiety levels (94.7%) compared to those living with family (86.0%) (p = 0.019). Similarly, participants who reported stressful life events showed a greater prevalence of anxiety (90.5%) than those who did not (83.2%) (p = 0.007). Table 2 Participant Characteristics and Bivariate Associations with Depression and Anxiety Predictors Total Depression p-value Anxiety p-value n (%) n (%) n (%) Total 640 100% 530 82.8% 559 87.3% PM2.5 0.004 0.490 Moderate 214 33.7% 164 76.6% 184 86.0% Unhealthy 422 66.4% 362 85.8% 371 87.9% PM10 < 0.001 0.386 Moderate 309 48.6% 236 76.4% 266 47.9% Unhealthy 327 51.4% 290 88.7% 289 52.1% Age (in years) < 0.001 < 0.001 18–29 249 38.9% 186 74.7% 196 78.7% 30–44 242 37.8% 210 86.8% 223 92.2% 45+ 149 23.3% 134 90.0% 140 93.9% Gender < 0.001 < 0.001 Female 328 51.6% 246 75.0% 267 81.4% Male 308 48.4% 280 90.9% 288 93.5% Nationality 0.318 0.132 Non-Kuwaiti 126 19.8% 108 85.7% 115 91.3% Kuwaiti 510 80.2% 418 81.9% 440 86.3% Education level 0.029 0.138 Highschool or less 180 28.3% 157 87.2% 163 90.6% Diploma 202 31.8% 171 84.7% 178 88.1% University or higher 254 39.9% 198 78% 214 84.3% Employment status < 0.001 < 0.001 Not employed/ retired/ student 208 32.7% 154 74.0% 159 76.4% Self /Part-time employed 251 39.5% 227 90.4% 242 96.4% Full-time employed 177 27.8% 145 81.9% 154 87.0% Income level (KD) 0.804 0.398 1500 64 10.1% 52 81.3% 55 85.9% Marital status < 0.001 < 0.001 Single/divorced/widowed 239 37.6% 180 75.3% 189 79.1% Married 397 62.4% 346 87.2% 366 92.2% Smoking status < 0.001 < 0.001 Non-smoker/Ex-smoker 391 61.5% 303 77.5% 318 81.3% Smoker 245 38.5% 223 91.0% 237 96.7% BMI (kg/m) 0.037 0.259 Normal 330 52.2% 275 83.3% 289 87.6% Overweight 240 38.0% 203 84.6% 212 88.3% Obese 62 9.8% 44 71.0% 50 80.7% Physical activity status 0.510 0.101 Inactive 561 88.2% 466 83.1% 494 88.1% Active 75 11.8% 60 80.0% 61 81.3% Living status 0.006 0.019 With family 542 85.2% 439 81.0% 466 86.0% Alone / With others 94 14.8% 87 92.6% 89 94.7% Stressful life events < 0.001 0.007 No stressful life events. 280 44.0% 214 76.4% 233 83.2% Stressful life events 356 56.0% 312 87.6% 322 90.5% *The p-values are obtained from the chi-square test. Multivariable Predictors of Depression As shown in Table 3 , participants exposed to unhealthy PM2.5 levels had 1.7 times higher odds of reporting depression compared to those in areas with moderate levels (95% CI: 1.05–2.59, p = 0.029). A stronger association was observed for PM10, where the odds of depression were 2.9 times higher among those exposed to unhealthy levels (95% CI: 1.82–4.68, p < 0.001). Male participants had significantly higher odds of depression than females (OR = 2.8; 95% CI: 1.71–4.67, p < 0.001). Marital status was also associated with depression; married individuals had 2.2 times the odds of experiencing depression compared to those who were single, divorced, or widowed (95% CI: 1.34–3.47, p = 0.002). Living arrangement was another significant factor. Those living with non-family members or alone had 3.1 times the odds of depression compared to those living with family (95% CI: 1.31–7.11, p = 0.009). Finally, experiencing stressful life events was associated with twice the odds of depression compared to those without such experiences (OR = 2.1; 95% CI: 1.33–3.28, p = 0.001). Table 3 Adjusted Odds Ratios for Depression by Exposure and Individual-Level Predictors Predictors Depression ORs [95% C.I.] p-value PM2.5 Moderate [ref] Unhealthy 1.7 [1.05, 2.59] 0.029 PM10 Moderate [ref] Unhealthy 2.9 [1.82, 4.68] < 0.001 Gender Female [ref] Male 2.8 [1.71, 4.67] < 0.001 Marital status Single/divorced/widowed [ref] Married 2.2 [1.34, 3.47] 0.002 Living status With family [ref] Alone / With others 3.1 [1.31, 7.11] 0.009 Stressful life events No stressful life events [ref] Stressful life events 2.1 [1.33, 3.28] 0.001 *Each predictor is adjusted for the other predictors in the table. Multivariable Predictors of Anxiety As shown in Table 4 , multivariable analysis revealed no significant association between anxiety and air quality after adjusting for other variables. This suggests that, unlike depression, PM2.5 and PM10 exposure were not independent predictors of anxiety in this population. Several other variables, however, remained significantly associated with anxiety. Participants who were self-employed or working part-time had 5.3 times the odds of experiencing anxiety compared to those who were unemployed, retired, or students (95% CI: 2.44–11.64, p < 0.001). Although full-time employment was associated with slightly higher odds of anxiety (OR = 1.6), this result did not reach statistical significance (95% CI: 0.89–2.80, p = 0.117). Smoking status was a strong predictor: smokers had 4.5 times higher odds of anxiety compared to non-smokers or ex-smokers (95% CI: 2.03–9.93, p < 0.001). Living arrangement also showed a significant effect—those living alone or with non-family members had 3.3 times the odds of anxiety compared to those living with family (95% CI: 1.28–8.73, p = 0.013). Finally, individuals who reported experiencing stressful life events had significantly higher odds of anxiety than those who did not (OR = 2.1; 95% CI: 1.27–3.47, p = 0.004). Table 4 Adjusted Odds Ratios for Anxiety by Exposure and Individual-Level Predictors Predictors Anxiety ORs [95% C.I.] p-value Employment status Not employed/ retired/ student [ref] Self /Part-time employed 5.3 [2.44, 11.64] < 0.001 Full-time employed 1.6 [0.89, 2.80] 0.117 Smoking status Non-smoker/Ex-smoker [ref] Smoker 4.5 [2.03, 9.93] < 0.001 Living status With family [ref] Alone / With others 3.3 [1.28, 8.73] 0.013 Stressful life events No stressful life events [ref] Stressful life events 2.1 [1.27, 3.47] 0.004 *Each predictor is adjusted for the other predictors in the table. Discussion This is one of the first studies in the Middle East that examines the relationship between particulate matter (PM) exposure and adult mental health, using a well-established and globally validated measure to assess anxiety and depression. By integrating environmental data with these mental health measurements, we were able to analyze the connection in a local Kuwaiti context while also making accurate worldwide comparisons. Our findings add valuable regional data to the burgeoning literature on air pollution and mental health, highlighting the need of context-sensitive public health planning. We found a substantial relationship between elevated PM levels and a higher likelihood of depression. Individuals exposed to unhealthy PM2.5 levels had a 70% greater likelihood of experiencing depression, while those exposed to unhealthy PM10 levels were almost three times as likely to report depressive. These findings are consistent with previous studies in China and India that have linked long-term PM2.5 exposure to depression ( 4 , 27 ). While conditions and exposure levels vary among locations, the consistent trend supports the need for prioritizing air quality solutions. Reducing PM might reduce not only respiratory and cardiovascular loads, but also psychological ones, demonstrating the dual effect of environmental control( 28 , 29 ). Unlike depression, anxiety symptoms were not substantially attributed to PM exposure after accounting for other factors. This contrasts with studies conducted in the United States and South Korea, where anxiety levels increased with poor air quality ( 30 , 31 ). One probable explanation is the choice of assessment tool: our study utilized the DASS-21, whereas others used alternative tools, such as the Beck Depression Inventory or the GHQ. Cultural norms and individual’s symptom may potentially impact outcomes ( 32 ). Future research might use multiple anxiety assessments across varied demographics to obtain a comprehensive understanding. Interestingly, our data revealed a higher prevalence of depression among men, which contradicts findings from most Western literature, where depression is more typically reported among women ( 33 ). Cultural expectations of emotional control, particularly among men, may cause delayed aid-seeking or underreporting. Furthermore, social pressures and insufficient male-focused mental health services may contribute to this tendency ( 34 ). These findings indicate the necessity for gender-sensitive mental health efforts that reflect the local cultural context. Another noteworthy finding was that self-employed and part-time workers reported much higher levels of anxiety than jobless individuals. This contrasts with worldwide trends, which frequently identify unemployment as a significant risk factor for anxiety ( 31 ). In Kuwait, job instability and irregular income among part-time and self-employed workers may cause greater stress than unemployment itself. Addressing this may necessitate tailored mental health and financial support networks for these at-risk occupations. We additionally identified a robust association between smoking and anxiety: smokers were more than four times more likely to experience anxious symptoms. This complements earlier studies relating tobacco use to mental health issues ( 35 ). These findings emphasize the significance of integrated methods that incorporate mental health treatment and smoking cessation programs. Coordinated interventions at the primary care level, as suggested by international public health authorities, may be very beneficial ( 36 ). Married individuals were twice as likely to experience depression as their single, divorced, or widowed counterparts. Although marriage is often perceived as protective, it can sometimes cause stress due to interpersonal conflict, caregiving responsibilities, or financial pressure ( 37 , 38 ), These findings emphasize the need of including mental health assistance within family and couples counseling. Public health systems should investigate expanding access to marriage-related mental health care ( 39 ). According to previous study, those who live alone or with non-family members had greater rates of depression and anxiety ( 40 ). This tendency may be especially important for expatriates in Kuwait, who are more inclined to live away from family. Programs that promote community integration, such as orientation workshops, support groups, and access to culturally relevant therapy, can contribute to lowering these risks ( 41 ). Stressful life situations were also a substantial predictor of mental health symptoms. Participants who described such incidents were twice as likely to report depression and three times as likely to report anxiety. These findings are consistent with an extensive body of research on stress and mental health ( 42 , 43 ). For high-risk groups, public health interventions should include preventative efforts such as resilience building, early screening, and stress-reduction initiatives. The cross-sectional design limits our capacity to infer causality. Longitudinal studies will be required to investigate the causal links between PM exposure and mental health over time. Furthermore, the convenience and snowball sampling methods we used may restrict the sample's representativeness. Despite these limitations, the findings provide a solid foundation for future study and highlight the importance of tailored, locally customized mental health treatments in Kuwait. Conclusion Our research revealed that higher exposure to particulate matter (PM2.5 and PM10) was linked to an increased likelihood of depression in Kuwaiti adults. We did not find such a correlation with anxiety, which may be due to differences in the way anxiety symptoms manifest or are assessed in connection to environmental exposures. In addition to pollution, factors that affected mental health outcomes included housing arrangements, smoking habits, gender, and marital status. These results add crucial regional context to the growing global body of research demonstrating a connection between air pollution and mental health. They also act as a reminder that when talking about environmental health, mental health should not be disregarded. Future studies will be crucial, especially longitudinal ones that assess a wide range of populations. To mitigate the mental health concerns connected with pollution, efforts must extend beyond just monitoring air quality. Building healthier, more resilient communities will be facilitated by raising public awareness, enacting supportive laws, and implementing programs that give mental health top priority in environmental planning. Abbreviations AQG levels – Air Quality Guidelines BMI – Body Mass Index DASS-21 – The Depression, Anxiety, and Stress Scale - 21 Items EPA – Environmental Public Authority PM10 – Particulate Matter 10 PM2.5 – Particulate Matter 2.5 WHO – World Health Organization Declarations Ethics approval and consent to participate This study was approved by the Standing Committee for Coordination of Health and Medical Research at the Ministry of Health, Kuwait (Reference No. 2024/2527). All participants provided electronic informed consent before participating in the survey. Participation was entirely voluntary, and all data were collected anonymously to ensure confidentiality Consent for publication Not applicable Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors received no specific funding for this work. Authors' contributions AA conceived the study, designed the questionnaire, conducted data collection, and performed the statistical analysis. EA played a leading supervisory role and contributed substantially to the interpretation of findings. Both authors reviewed, revised, and approved the final manuscript. Acknowledgements Not applicable. References Verhoeven JE, Révész D, van Oppen P, Epel ES, Wolkowitz OM, Penninx BWJH. Anxiety disorders and accelerated cellular ageing. Br J Psychiatry J Ment Sci. 2015;206(5):371–8. Vigo D, Thornicroft G, Atun R. Estimating the true global burden of mental illness. Lancet Psychiatry. 2016;3(2):171–8. Yang T, Wang J, Huang J, Kelly FJ, Li G. Long-term Exposure to Multiple Ambient Air Pollutants and Association With Incident Depression and Anxiety. JAMA Psychiatry. 2023;80(4):305–13. Braithwaite I, Zhang S, Kirkbride JB, Osborn DPJ, Hayes JF. Air Pollution (Particulate Matter) Exposure and Associations with Depression, Anxiety, Bipolar, Psychosis and Suicide Risk: A Systematic Review and Meta-Analysis. Environ Health Perspect. 2019;127(12):126002. GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Lond Engl. 2020;396(10258):1223–49. Manisalidis I, Stavropoulou E, Stavropoulos A, Bezirtzoglou E. Environmental and Health Impacts of Air Pollution: A Review. Front Public Health. 2020;8:14. WHO. Ambient air pollution: A global assessment of exposure and burden of disease [Internet]. 2016 [cited 2025 Apr 7]. Available from: https://www.who.int/publications/i/item/9789241511353 Buoli M, Grassi S, Caldiroli A, Carnevali GS, Mucci F, Iodice S, et al. Is there a link between air pollution and mental disorders? Environ Int. 2018;118:154–68. Weitekamp CA, Hofmann HA. Effects of air pollution exposure on social behavior: a synthesis and call for research. Environ Health Glob Access Sci Source. 2021;20(1):72. Cipriani G, Danti S, Carlesi C, Borin G. Danger in the Air: Air Pollution and Cognitive Dysfunction. Am J Alzheimers Dis Other Demen. 2018;33(6):333–41. Liu Q, Wang W, Gu X, Deng F, Wang X, Lin H, et al. Association between particulate matter air pollution and risk of depression and suicide: a systematic review and meta-analysis. Environ Sci Pollut Res Int. 2021;28(8):9029–49. Zundel CG, Ryan P, Brokamp C, Heeter A, Huang Y, Strawn JR, et al. Air pollution, depressive and anxiety disorders, and brain effects: A systematic review. Neurotoxicology. 2022;93:272–300. Yang Z, Song Q, Li J, Zhang Y, Yuan XC, Wang W, et al. Air pollution and mental health: the moderator effect of health behaviors. Environ Res Lett. 2021;16(4):044005. Petrowski K, Bührer S, Strauß B, Decker O, Brähler E. Examining air pollution (PM10), mental health and well-being in a representative German sample. Sci Rep. 2021;11(1):18436. Jung M, Cho D, Shin K. The Impact of Particulate Matter on Outdoor Activity and Mental Health: A Matching Approach. Int J Environ Res Public Health. 2019;16(16):2983. Lopez-Duran NL, Kovacs M, George CJ. Hypothalamic-pituitary-adrenal axis dysregulation in depressed children and adolescents: a meta-analysis. Psychoneuroendocrinology. 2009;34(9):1272–83. Block ML, Calderón-Garcidueñas L. Air pollution: mechanisms of neuroinflammation and CNS disease. Trends Neurosci. 2009;32(9):506–16. Li H, Cai J, Chen R, Zhao Z, Ying Z, Wang L, et al. Particulate Matter Exposure and Stress Hormone Levels: A Randomized, Double-Blind, Crossover Trial of Air Purification. Circulation. 2017;136(7):618–27. Alahmad B, Al-Hemoud A, Kang CM, Almarri F, Kommula V, Wolfson JM, et al. A two-year assessment of particulate air pollution and sources in Kuwait. Environ Pollut Barking Essex 1987. 2021;282:117016. Al-Hemoud A, Al-Khayat A, Al-Dashti H, Li J, Alahmad B, Koutrakis P. PM2.5 and PM10 during COVID-19 lockdown in Kuwait: Mixed effect of dust and meteorological covariates. Environ Chall Amst Neth. 2021;5:100215. Khatatbeh M, Alzoubi K, Khabour O, Al-Delaimy W. Adverse Health Impacts of Living Near an Oil Refinery in Jordan. Environ Health Insights. 2020;14:1178630220985794. Neira M, Erguler K, Ahmady-Birgani H, AL-Hmoud ND, Fears R, Gogos C, et al. Climate change and human health in the Eastern Mediterranean and Middle East: Literature review, research priorities and policy suggestions. Environ Res. 2023;216:114537. Abdul-Wahab S, Bouhamra W, Ettouney H, Sowerby B, Crittenden BD. Analysis of air pollution at Shuaiba Industrial Area in Kuwait. Toxicol Environ Chem. 2000;78(3–4):213–32. Gomez R, Summers M, Summers A, Wolf A, Summers J. Depression Anxiety Stress Scales-21: Measurement and Structural Invariance Across Ratings of Men and Women. Assessment. 2014;21(4):418–26. Zwolinsky S, McKenna J, Pringle A, Widdop P, Griffiths C. Physical activity assessment for public health: efficacious use of the single-item measure. Public Health. 2015;129(12):1630–6. Vert C, Sánchez-Benavides G, Martínez D, Gotsens X, Gramunt N, Cirach M, et al. Effect of long-term exposure to air pollution on anxiety and depression in adults: A cross-sectional study. Int J Hyg Environ Health. 2017;220(6):1074–80. Qiu H, Zhu X, Wang L, Pan J, Pu X, Zeng X, et al. Attributable risk of hospital admissions for overall and specific mental disorders due to particulate matter pollution: A time-series study in Chengdu, China. Environ Res. 2019;170:230–7. Pinault L, Thomson EM, Christidis T, Colman I, Tjepkema M, van Donkelaar A, et al. The association between ambient air pollution concentrations and psychological distress. Health Rep. 2020;31(7):3–11. Radua J, De Prisco M, Oliva V, Fico G, Vieta E, Fusar-Poli P. Impact of air pollution and climate change on mental health outcomes: an umbrella review of global evidence. World Psychiatry Off J World Psychiatr Assoc WPA. 2024;23(2):244–56. Choi KH, Bae S, Kim S, Kwon HJ. Indoor and outdoor PM2.5 exposure, and anxiety among schoolchildren in Korea: a panel study. Environ Sci Pollut Res. 2020;27(22):27984–94. Pun VC, Manjourides J, Suh H. Association of Ambient Air Pollution with Depressive and Anxiety Symptoms in Older Adults: Results from the NSHAP Study. Environ Health Perspect. 2017;125(3):342–8. Altweck L, Marshall TC, Ferenczi N, Lefringhausen K. Mental health literacy: a cross-cultural approach to knowledge and beliefs about depression, schizophrenia and generalized anxiety disorder. Front Psychol. 2015;6:1272. Seedat S, Scott KM, Angermeyer MC, Berglund P, Bromet EJ, Brugha TS, et al. Cross-national associations between gender and mental disorders in the WHO World Mental Health Surveys. Arch Gen Psychiatry. 2009;66(7):785. Abu-Hamda B, Soliman A, Babekr A, Bellaj T. Emotional Expression and Culture: Implications from Nine Arab Countries. Eur Psychiatry. 2017;41(S1):S230–230. Fluharty M, Taylor AE, Grabski M, Munafò MR. The Association of Cigarette Smoking With Depression and Anxiety: A Systematic Review. Nicotine Tob Res Off J Soc Res Nicotine Tob. 2017;19(1):3–13. U.S. Department of Health and Human Services. Smoking Cessation: A Report of the Surgeon General (Executive Summary). 2020. Robles TF, Slatcher RB, Trombello JM, McGinn MM. Marital quality and health: A meta-analytic review. Psychol Bull. 2014;140(1):140–87. Whisman MA. Marital distress and DSM-IV psychiatric disorders in a population-based national survey. J Abnorm Psychol. 2007;116(3):638–43. Doss BD, Rhoades GK, Stanley SM, Markman HJ. Marital therapy, retreats, and books: The who, what, when, and why of relationship help-seeking. J Marital Fam Ther. 2009;35(1):18–29. Cacioppo S, Capitanio JP, Cacioppo JT. Toward a Neurology of Loneliness. Psychol Bull. 2014;140(6):1464–504. Kaladchibachi S, Al-Dhafiri AM. Mental health care in Kuwait: Toward a community-based decentralized approach. Int Soc Work. 2018;61(3):329–34. Goyal M, Singh S, Sibinga EMS, Gould NF, Rowland-Seymour A, Sharma R, et al. Meditation Programs for Psychological Stress and Well-being: A Systematic Review and Meta-analysis. JAMA Intern Med. 2014;174(3):357–68. Kessler RC. The effects of stressful life events on depression. Annu Rev Psychol. 1997;48:191–214. 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The co-occurrence of two or more mental health conditions may significantly reduce life expectancy by exacerbating age-related illnesses (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). With the rising prevalence of depressive and anxiety disorders, there is an urgent need for public health efforts to focus on prevention and to identify modifiable environmental risk factors associated with these conditions (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe increasing burden of these disorders, coupled with ambiguity around their causes, has intensified interest in investigating environmental exposures as potential contributors (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Among these, anxiety and depression have gained attention for their possible links to air pollution (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Ambient air pollution, a recognized environmental health risk since the 1990s (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), is largely attributed to factors such as urbanization, industrial activity, and widespread reliance on internal combustion engine vehicles\u0026mdash;all of which have substantial implications for public health (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is growing evidence that air pollutants may influence mental health through toxic effects on the central nervous system (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Findings from epidemiological and animal studies suggest that exposure to polluted air can lead to neurodevelopmental and neuropsychiatric symptoms (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In humans, such exposure may affect areas of the brain responsible for emotional regulation, potentially increasing the risk of depression, anxiety, eating disorders, and addictive behaviors (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Indirect consequences of air pollution may also include decreased labor productivity, lower income, and heightened job insecurity\u0026mdash;factors associated with poor mental health outcomes (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Other indirect effects, such as increased irritability, reduced outdoor activity, and a general decline in well-being, have also been reported (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParticulate matter (PM), a complex mixture of airborne particles, is a major component of air pollution and has been increasingly implicated in both physical and mental health effects (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). It has been proposed that neuroinflammation, involving inflammatory responses in the central nervous system (CNS), plays an important role in the development of depression (Y. Liu et al., 2012). In addition, disruption of the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal (HPA) axis has been linked to depressive symptoms (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). PM exposure has been associated with elevated cortisol levels, oxidative stress, glial cell activation, and structural changes in the brain\u0026mdash;all of which support a biological basis for its effects on mental health (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Kuwait, environmental conditions and human activities contribute to elevated levels of air pollution. PM2.5 concentrations in Kuwait City have been reported at levels four times higher than the U.S. National Ambient Air Quality Standards\u0026rsquo; recommended annual limit (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Similarly, PM10 concentrations have been found to be high due to extreme weather patterns, including elevated temperatures and wind speeds (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Given Kuwait\u0026rsquo;s hot desert climate and its petroleum-driven economy, high pollution levels are to be expected (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe major contributors to air pollution in Kuwait include emissions from vehicles, burning of waste, and activities related to the extraction, storage, and transportation of fossil fuels. These processes, particularly those involving heavy machinery and transport vehicles, are known sources of PM emissions (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study investigates the association between exposure to PM2.5 and PM10 and the prevalence of depression and anxiety among adults living in six urban areas in Kuwait. Data on air pollution were obtained from the Kuwait Environmental Public Authority (EPA), while information on mental health outcomes and related predictors was collected via a structured online questionnaire.\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003eThis study aimed to examine the association between long-term exposure to PM2.5 and PM10 and symptoms of depression and anxiety among adults in Kuwait. From January 2022 to October 2023, the Kuwait Environmental Public Authority (EPA) monitored levels of particulate matter (PM) across 12 regions in the country. Six areas\u0026mdash;Al-Ahmadi, Al-Fahaheel, Al-Jahra, Al-Salam, Saad Al-Abdullah, and Al-Zahra\u0026mdash;were randomly selected for analysis. We conducted a cross-sectional study to assess the prevalence of depression and anxiety among adults residing in these selected areas. A validated and reliable self-administered online questionnaire was used to collect the data. Participation was voluntary and open to all adult residents, including both men and women, aged 18 to 65 years.\u003c/p\u003e \u003cp\u003eThe main exposures in this study were the concentrations of PM2.5 and PM10. These data were obtained from daily EPA measurements taken over a 22-month period in the six selected areas. Average concentrations were calculated for each location. Additionally, the Air Quality Index (AQI) was used to categorize the pollution levels. AQI values between 0 and 100 were classified as \u0026ldquo;moderate,\u0026rdquo; while values from 101 to 200 were classified as \u0026ldquo;unhealthy.\u0026rdquo; The \u0026ldquo;unhealthy\u0026rdquo; category included both levels considered harmful to sensitive groups and to the general population.\u003c/p\u003e \u003cp\u003eThe online questionnaire was distributed to eligible adults living in the six selected areas. To reach the target population, we employed a combination of convenience and snowball sampling techniques. The survey link was made available at local supermarkets and other public spaces, and participants were encouraged to share it within their communities.\u003c/p\u003e \u003cp\u003eDepression and anxiety were measured using the 21-item Depression, Anxiety, and Stress Scale (DASS-21). Each subscale comprised seven items, and responses were rated on a four-point Likert scale ranging from 0 (did not apply at all) to 3 (applied most of the time). The subscale scores were doubled prior to analysis. For depression, a total score above 10 indicated symptoms of depression. For anxiety, a score of 8 or higher was used to identify the presence of anxiety symptoms, based on established cutoff values (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe questionnaire also included demographic, socioeconomic, and lifestyle-related variables that could act as potential confounders. These included age, residential area, gender, nationality, education level, employment status, income, marital status, smoking status, and living arrangement. Participants were also asked about stressful life events. Additionally, they provided self-reported weight (in kilograms) and height (in centimetres), from which the body mass index (BMI) was calculated. BMI was classified as underweight (\u0026lt;\u0026thinsp;18.5), normal (18.5\u0026ndash;24.9), overweight (25\u0026ndash;29.9), or obese (\u0026ge;\u0026thinsp;30), following CDC guidelines (CDC, 2023). Physical activity was assessed by asking how many days per week participants engaged in moderate to vigorous exercise. Those reporting five or more days per week were considered sufficiently active (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eAccording to the literature, the mean odds ratio (OR) for depression and anxiety associated with outdoor air pollution exposure was approximately 2.8 (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Accordingly, the minimum sample size was calculated to be 310, achieving a power of 80% and a significance level of 0.05, calculated using STATA 15. However, to enhance the accuracy of representing the population and to yield more robust results that account for the use of non-probability sampling, we have decided to double our sample size to 620.\u003c/p\u003e \u003cp\u003eThe primary outcome variables were depression (yes/no) and anxiety (yes/no), based on the DASS-21 thresholds. The main independent variables were PM2.5 and PM10 AQI categories (moderate or unhealthy). Additional independent variables included demographic, socioeconomic, and lifestyle factors.\u003c/p\u003e \u003cp\u003eAll analyses were conducted using STATA software version 18. Descriptive statistics were presented as frequencies and percentages. Chi-square tests were used to examine associations between categorical independent and dependent variables. Univariate logistic regression models were used to assess the individual associations of each predictor with depression and anxiety. Predictors with a significance level below 0.1 in univariate analysis were included in the multivariable logistic regression models.\u003c/p\u003e \u003cp\u003eSeparate multivariable models were estimated to examine predictors of depression and anxiety, respectively. A backward elimination approach was used to identify the final set of predictors in each model. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. To identify confounding variables, changes in beta coefficients were assessed before and after inclusion in the model. Variables that altered the coefficient by more than 10% were classified as confounders. Model fit was assessed using the Goodness-of-Fit test, the Likelihood Ratio Test (LRT), and the Akaike Information Criterion (AIC). Multicollinearity among predictors was also evaluated in the final models.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eUsing mean concentrations to calculate the Air Quality Index (AQI) for PM2.5, we found that Al-Jahra and Al-Zahra exhibited moderate levels. In contrast, Al-Salam\u0026mdash;located near Al-Zahra\u0026mdash;and Saad Al-Abdullah\u0026mdash;adjacent to Al-Jahra\u0026mdash;both had unhealthy PM2.5 levels. Similarly, the adjacent areas of Al-Ahmadi and Al-Fahaheel also showed unhealthy PM2.5 concentrations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor PM10, the AQI indicated that Al-Zahra and Al-Salam had moderate levels, while Al-Ahmadi recorded an unhealthy PM10 level. Despite being in close proximity, Al-Fahaheel also had an unhealthy PM10 level. Likewise, the neighboring regions of Saad Al-Abdullah and Al-Jahra reported unhealthy PM10 concentrations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eThe Air Quality Index (AQI) level for PM2.5 and PM10 by the residential area from January 2022 to October 2023.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidential areas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePM2.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePM10\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAQI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(level of concern)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAQI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(level of concern)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl-Ahmadi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl-Fahaheel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnhealthy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl-Jahra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnhealthy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl-Salam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl-Zahra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaad Al-Abdullah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnhealthy\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\u003eDescriptive Summary\u003c/p\u003e \u003cp\u003eA total of 640 adults participated in the study (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Of these, 530 participants (82.8%) reported symptoms of depression, and 559 (87.3%) reported symptoms of anxiety. Regarding air quality exposure, 66.4% of the participants lived in areas classified as having unhealthy levels of PM2.5, while 51.4% were exposed to unhealthy PM10 levels.\u003c/p\u003e \u003cp\u003eParticipants were relatively evenly distributed across the 18\u0026ndash;29 (38.9%) and 30\u0026ndash;44 (37.8%) age groups, with 23.3% aged 45 years or older. Gender distribution was balanced, with 51.6% female and 48.4% male participants. The majority (80.2%) were Kuwaiti nationals. In terms of educational attainment, 39.9% held a university degree or higher, 31.8% had a diploma, and 28.3% had completed high school or less.\u003c/p\u003e \u003cp\u003eFor employment status, 39.5% were self-employed or worked part-time, 27.8% were full-time employees, and 32.7% were unemployed, retired, or students. Income distribution showed that 47.2% earned less than 500 KD per month, 42.8% earned between 500\u0026ndash;1500 KD, and 10.1% earned over 1500 KD. Most participants were married (62.4%).\u003c/p\u003e \u003cp\u003eWith respect to health and lifestyle factors, 61.5% were non-smokers or ex-smokers. A normal body mass index (BMI) was reported by 52.2%, while 38.0% were overweight and 9.8% were obese. A large majority (88.2%) reported being physically inactive. Most participants lived with family (85.2%), and more than half (56.0%) reported experiencing stressful life events.\u003c/p\u003e \u003cp\u003eAll predictor variables were assessed in relation to the prevalence of depression. A significant association was observed between air quality and depression (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among those exposed to moderate PM2.5 levels, 76.6% reported symptoms of depression, compared to 85.8% of participants exposed to unhealthy PM2.5 levels (p\u0026thinsp;=\u0026thinsp;0.004). A similar pattern was found for PM10 exposure: 76.4% of individuals exposed to moderate levels reported depression, versus 88.7% of those exposed to unhealthy PM10 levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eFactors Associated with Depression\u003c/p\u003e \u003cp\u003eA significant association was found between air quality and depression. Participants exposed to unhealthy levels of PM2.5 and PM10 reported higher depression rates (85.8% and 88.7%, respectively) compared to those exposed to moderate levels (76.6% and 76.4%, respectively; p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDepression prevalence increased with age, from 74.7% in those aged 18\u0026ndash;29 to 90% in participants aged 45 and above. Men reported a notably higher rate of depression than women (91% vs. 75%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Lower educational attainment was also associated with greater depression prevalence, particularly among those with a high school diploma or less.\u003c/p\u003e \u003cp\u003eEmployment status and marital status showed clear trends: higher depression rates were observed among part-time workers and married individuals. Smoking was strongly linked to depression, with 91% of smokers affected versus 77.5% of non-smokers or former smokers. Living with people other than family and reporting stressful life events were also significantly associated with higher depression prevalence (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eFactors Associated with Anxiety\u003c/p\u003e \u003cp\u003eAnxiety prevalence increased with age, reaching 94.0% among participants aged 45 and over, compared to 92.2% in those aged 30\u0026ndash;44 and 78.7% in the youngest age group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A significant gender difference was also observed, with higher anxiety rates reported among men (93.5%) than women (81.4%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmployment status was strongly associated with anxiety. Nearly all self-employed or part-time workers reported symptoms (96.4%), followed by 87.6% of full-time employees and 76.4% of those unemployed, retired, or students (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Anxiety was more common among married individuals (92.2%) compared to those who were single, divorced, or widowed (79.1%).\u003c/p\u003e \u003cp\u003eSmoking was another significant factor: 96.7% of smokers experienced anxiety versus 81.3% of non-smokers or former smokers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Those living alone or with non-family members also reported higher anxiety levels (94.7%) compared to those living with family (86.0%) (p\u0026thinsp;=\u0026thinsp;0.019). Similarly, participants who reported stressful life events showed a greater prevalence of anxiety (90.5%) than those who did not (83.2%) (p\u0026thinsp;=\u0026thinsp;0.007).\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\u003eParticipant Characteristics and Bivariate Associations with Depression and Anxiety\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e87.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePM2.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e86.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e87.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePM10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e47.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e52.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (in years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\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\"\u003e \u003cp\u003e18\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e92.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e93.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\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\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e81.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e93.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNationality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Kuwaiti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e91.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKuwaiti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e86.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighschool or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e90.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e84.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\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\"\u003e \u003cp\u003eNot employed/ retired/ student\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e76.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf /Part-time employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e96.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull-time employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e87.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome level (KD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e85.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u0026ndash;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e89.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e85.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\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\"\u003e \u003cp\u003eSingle/divorced/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e79.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e92.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\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\"\u003e \u003cp\u003eNon-smoker/Ex-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e81.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e96.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e87.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e80.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e81.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e86.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlone / With others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e94.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStressful life events\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo stressful life events.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e83.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStressful life events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e90.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e*The p-values are obtained from the chi-square test.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMultivariable Predictors of Depression\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, participants exposed to unhealthy PM2.5 levels had 1.7 times higher odds of reporting depression compared to those in areas with moderate levels (95% CI: 1.05\u0026ndash;2.59, p\u0026thinsp;=\u0026thinsp;0.029). A stronger association was observed for PM10, where the odds of depression were 2.9 times higher among those exposed to unhealthy levels (95% CI: 1.82\u0026ndash;4.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eMale participants had significantly higher odds of depression than females (OR\u0026thinsp;=\u0026thinsp;2.8; 95% CI: 1.71\u0026ndash;4.67, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Marital status was also associated with depression; married individuals had 2.2 times the odds of experiencing depression compared to those who were single, divorced, or widowed (95% CI: 1.34\u0026ndash;3.47, p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e \u003cp\u003eLiving arrangement was another significant factor. Those living with non-family members or alone had 3.1 times the odds of depression compared to those living with family (95% CI: 1.31\u0026ndash;7.11, p\u0026thinsp;=\u0026thinsp;0.009). Finally, experiencing stressful life events was associated with twice the odds of depression compared to those without such experiences (OR\u0026thinsp;=\u0026thinsp;2.1; 95% CI: 1.33\u0026ndash;3.28, p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted Odds Ratios for Depression by Exposure and Individual-Level Predictors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eORs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[95% C.I.]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePM2.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[ref]\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=\"c1\"\u003e \u003cp\u003eUnhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.05, 2.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePM10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[ref]\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=\"c1\"\u003e \u003cp\u003eUnhealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.82, 4.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[ref]\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=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.71, 4.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle/divorced/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[ref]\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=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.34, 3.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[ref]\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=\"c1\"\u003e \u003cp\u003eAlone / With others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.31, 7.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStressful life events\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo stressful life events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[ref]\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=\"c1\"\u003e \u003cp\u003eStressful life events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.33, 3.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Each predictor is adjusted for the other predictors in the table.\u003c/p\u003e \u003cp\u003eMultivariable Predictors of Anxiety\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, multivariable analysis revealed no significant association between anxiety and air quality after adjusting for other variables. This suggests that, unlike depression, PM2.5 and PM10 exposure were not independent predictors of anxiety in this population.\u003c/p\u003e \u003cp\u003eSeveral other variables, however, remained significantly associated with anxiety. Participants who were self-employed or working part-time had 5.3 times the odds of experiencing anxiety compared to those who were unemployed, retired, or students (95% CI: 2.44\u0026ndash;11.64, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although full-time employment was associated with slightly higher odds of anxiety (OR\u0026thinsp;=\u0026thinsp;1.6), this result did not reach statistical significance (95% CI: 0.89\u0026ndash;2.80, p\u0026thinsp;=\u0026thinsp;0.117).\u003c/p\u003e \u003cp\u003eSmoking status was a strong predictor: smokers had 4.5 times higher odds of anxiety compared to non-smokers or ex-smokers (95% CI: 2.03\u0026ndash;9.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Living arrangement also showed a significant effect\u0026mdash;those living alone or with non-family members had 3.3 times the odds of anxiety compared to those living with family (95% CI: 1.28\u0026ndash;8.73, p\u0026thinsp;=\u0026thinsp;0.013). Finally, individuals who reported experiencing stressful life events had significantly higher odds of anxiety than those who did not (OR\u0026thinsp;=\u0026thinsp;2.1; 95% CI: 1.27\u0026ndash;3.47, p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eAdjusted Odds Ratios for Anxiety by Exposure and Individual-Level Predictors\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eORs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[95% C.I.]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot employed/ retired/ student\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[ref]\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=\"c1\"\u003e \u003cp\u003eSelf /Part-time employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[2.44, 11.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\"\u003e \u003cp\u003eFull-time employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.89, 2.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smoker/Ex-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[ref]\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=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[2.03, 9.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\"\u003e \u003cp\u003e\u003cb\u003eLiving status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[ref]\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=\"c1\"\u003e \u003cp\u003eAlone / With others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.28, 8.73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStressful life events\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo stressful life events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[ref]\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=\"c1\"\u003e \u003cp\u003eStressful life events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.27, 3.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Each predictor is adjusted for the other predictors in the table.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is one of the first studies in the Middle East that examines the relationship between particulate matter (PM) exposure and adult mental health, using a well-established and globally validated measure to assess anxiety and depression. By integrating environmental data with these mental health measurements, we were able to analyze the connection in a local Kuwaiti context while also making accurate worldwide comparisons. Our findings add valuable regional data to the burgeoning literature on air pollution and mental health, highlighting the need of context-sensitive public health planning.\u003c/p\u003e \u003cp\u003eWe found a substantial relationship between elevated PM levels and a higher likelihood of depression. Individuals exposed to unhealthy PM2.5 levels had a 70% greater likelihood of experiencing depression, while those exposed to unhealthy PM10 levels were almost three times as likely to report depressive. These findings are consistent with previous studies in China and India that have linked long-term PM2.5 exposure to depression (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). While conditions and exposure levels vary among locations, the consistent trend supports the need for prioritizing air quality solutions. Reducing PM might reduce not only respiratory and cardiovascular loads, but also psychological ones, demonstrating the dual effect of environmental control(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnlike depression, anxiety symptoms were not substantially attributed to PM exposure after accounting for other factors. This contrasts with studies conducted in the United States and South Korea, where anxiety levels increased with poor air quality (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). One probable explanation is the choice of assessment tool: our study utilized the DASS-21, whereas others used alternative tools, such as the Beck Depression Inventory or the GHQ. Cultural norms and individual\u0026rsquo;s symptom may potentially impact outcomes (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Future research might use multiple anxiety assessments across varied demographics to obtain a comprehensive understanding.\u003c/p\u003e \u003cp\u003eInterestingly, our data revealed a higher prevalence of depression among men, which contradicts findings from most Western literature, where depression is more typically reported among women (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Cultural expectations of emotional control, particularly among men, may cause delayed aid-seeking or underreporting. Furthermore, social pressures and insufficient male-focused mental health services may contribute to this tendency (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). These findings indicate the necessity for gender-sensitive mental health efforts that reflect the local cultural context.\u003c/p\u003e \u003cp\u003eAnother noteworthy finding was that self-employed and part-time workers reported much higher levels of anxiety than jobless individuals. This contrasts with worldwide trends, which frequently identify unemployment as a significant risk factor for anxiety (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In Kuwait, job instability and irregular income among part-time and self-employed workers may cause greater stress than unemployment itself. Addressing this may necessitate tailored mental health and financial support networks for these at-risk occupations.\u003c/p\u003e \u003cp\u003eWe additionally identified a robust association between smoking and anxiety: smokers were more than four times more likely to experience anxious symptoms. This complements earlier studies relating tobacco use to mental health issues (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). These findings emphasize the significance of integrated methods that incorporate mental health treatment and smoking cessation programs. Coordinated interventions at the primary care level, as suggested by international public health authorities, may be very beneficial (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMarried individuals were twice as likely to experience depression as their single, divorced, or widowed counterparts. Although marriage is often perceived as protective, it can sometimes cause stress due to interpersonal conflict, caregiving responsibilities, or financial pressure (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), These findings emphasize the need of including mental health assistance within family and couples counseling. Public health systems should investigate expanding access to marriage-related mental health care (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to previous study, those who live alone or with non-family members had greater rates of depression and anxiety (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). This tendency may be especially important for expatriates in Kuwait, who are more inclined to live away from family. Programs that promote community integration, such as orientation workshops, support groups, and access to culturally relevant therapy, can contribute to lowering these risks (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStressful life situations were also a substantial predictor of mental health symptoms. Participants who described such incidents were twice as likely to report depression and three times as likely to report anxiety. These findings are consistent with an extensive body of research on stress and mental health (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). For high-risk groups, public health interventions should include preventative efforts such as resilience building, early screening, and stress-reduction initiatives.\u003c/p\u003e \u003cp\u003eThe cross-sectional design limits our capacity to infer causality. Longitudinal studies will be required to investigate the causal links between PM exposure and mental health over time. Furthermore, the convenience and snowball sampling methods we used may restrict the sample's representativeness. Despite these limitations, the findings provide a solid foundation for future study and highlight the importance of tailored, locally customized mental health treatments in Kuwait.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur research revealed that higher exposure to particulate matter (PM2.5 and PM10) was linked to an increased likelihood of depression in Kuwaiti adults. We did not find such a correlation with anxiety, which may be due to differences in the way anxiety symptoms manifest or are assessed in connection to environmental exposures. In addition to pollution, factors that affected mental health outcomes included housing arrangements, smoking habits, gender, and marital status.\u003c/p\u003e \u003cp\u003eThese results add crucial regional context to the growing global body of research demonstrating a connection between air pollution and mental health. They also act as a reminder that when talking about environmental health, mental health should not be disregarded. Future studies will be crucial, especially longitudinal ones that assess a wide range of populations.\u003c/p\u003e \u003cp\u003eTo mitigate the mental health concerns connected with pollution, efforts must extend beyond just monitoring air quality. Building healthier, more resilient communities will be facilitated by raising public awareness, enacting supportive laws, and implementing programs that give mental health top priority in environmental planning.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAQG levels – Air Quality Guidelines\u003c/p\u003e\n\u003cp id=\"_Toc152969652\"\u003eBMI – Body Mass Index\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc152969654\"\u003eDASS-21 – The Depression, Anxiety, and Stress Scale - 21 Items\u003c/p\u003e\n\u003cp\u003eEPA – Environmental Public Authority\u003c/p\u003e\n\u003cp id=\"_Toc152969657\"\u003ePM10 – Particulate Matter 10\u003c/p\u003e\n\u003cp id=\"_Toc152969658\"\u003ePM2.5 – Particulate Matter 2.5\u003c/p\u003e\n\u003cp id=\"_Toc152969660\"\u003eWHO – World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Standing Committee for Coordination of Health and Medical Research at the Ministry of Health, Kuwait (Reference No. 2024/2527). All participants provided electronic informed consent before participating in the survey. Participation was entirely voluntary, and all data were collected anonymously to ensure confidentiality\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\n\u003ch2\u003eAuthors' contributions\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAA conceived the study, designed the questionnaire, conducted data collection, and performed the statistical analysis. EA played a leading supervisory role and contributed substantially to the interpretation of findings. Both authors reviewed, revised, and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVerhoeven JE, R\u0026eacute;v\u0026eacute;sz D, van Oppen P, Epel ES, Wolkowitz OM, Penninx BWJH. Anxiety disorders and accelerated cellular ageing. Br J Psychiatry J Ment Sci. 2015;206(5):371\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVigo D, Thornicroft G, Atun R. Estimating the true global burden of mental illness. Lancet Psychiatry. 2016;3(2):171\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang T, Wang J, Huang J, Kelly FJ, Li G. Long-term Exposure to Multiple Ambient Air Pollutants and Association With Incident Depression and Anxiety. JAMA Psychiatry. 2023;80(4):305\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBraithwaite I, Zhang S, Kirkbride JB, Osborn DPJ, Hayes JF. Air Pollution (Particulate Matter) Exposure and Associations with Depression, Anxiety, Bipolar, Psychosis and Suicide Risk: A Systematic Review and Meta-Analysis. Environ Health Perspect. 2019;127(12):126002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Lond Engl. 2020;396(10258):1223\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManisalidis I, Stavropoulou E, Stavropoulos A, Bezirtzoglou E. Environmental and Health Impacts of Air Pollution: A Review. Front Public Health. 2020;8:14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. Ambient air pollution: A global assessment of exposure and burden of disease [Internet]. 2016 [cited 2025 Apr 7]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/9789241511353\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/9789241511353\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuoli M, Grassi S, Caldiroli A, Carnevali GS, Mucci F, Iodice S, et al. Is there a link between air pollution and mental disorders? Environ Int. 2018;118:154\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeitekamp CA, Hofmann HA. Effects of air pollution exposure on social behavior: a synthesis and call for research. Environ Health Glob Access Sci Source. 2021;20(1):72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCipriani G, Danti S, Carlesi C, Borin G. Danger in the Air: Air Pollution and Cognitive Dysfunction. Am J Alzheimers Dis Other Demen. 2018;33(6):333\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Q, Wang W, Gu X, Deng F, Wang X, Lin H, et al. Association between particulate matter air pollution and risk of depression and suicide: a systematic review and meta-analysis. Environ Sci Pollut Res Int. 2021;28(8):9029\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZundel CG, Ryan P, Brokamp C, Heeter A, Huang Y, Strawn JR, et al. Air pollution, depressive and anxiety disorders, and brain effects: A systematic review. Neurotoxicology. 2022;93:272\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Z, Song Q, Li J, Zhang Y, Yuan XC, Wang W, et al. Air pollution and mental health: the moderator effect of health behaviors. Environ Res Lett. 2021;16(4):044005.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetrowski K, B\u0026uuml;hrer S, Strau\u0026szlig; B, Decker O, Br\u0026auml;hler E. Examining air pollution (PM10), mental health and well-being in a representative German sample. Sci Rep. 2021;11(1):18436.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung M, Cho D, Shin K. The Impact of Particulate Matter on Outdoor Activity and Mental Health: A Matching Approach. Int J Environ Res Public Health. 2019;16(16):2983.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopez-Duran NL, Kovacs M, George CJ. Hypothalamic-pituitary-adrenal axis dysregulation in depressed children and adolescents: a meta-analysis. Psychoneuroendocrinology. 2009;34(9):1272\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlock ML, Calder\u0026oacute;n-Garcidue\u0026ntilde;as L. Air pollution: mechanisms of neuroinflammation and CNS disease. Trends Neurosci. 2009;32(9):506\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Cai J, Chen R, Zhao Z, Ying Z, Wang L, et al. Particulate Matter Exposure and Stress Hormone Levels: A Randomized, Double-Blind, Crossover Trial of Air Purification. Circulation. 2017;136(7):618\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlahmad B, Al-Hemoud A, Kang CM, Almarri F, Kommula V, Wolfson JM, et al. A two-year assessment of particulate air pollution and sources in Kuwait. Environ Pollut Barking Essex 1987. 2021;282:117016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Hemoud A, Al-Khayat A, Al-Dashti H, Li J, Alahmad B, Koutrakis P. PM2.5 and PM10 during COVID-19 lockdown in Kuwait: Mixed effect of dust and meteorological covariates. Environ Chall Amst Neth. 2021;5:100215.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhatatbeh M, Alzoubi K, Khabour O, Al-Delaimy W. Adverse Health Impacts of Living Near an Oil Refinery in Jordan. Environ Health Insights. 2020;14:1178630220985794.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeira M, Erguler K, Ahmady-Birgani H, AL-Hmoud ND, Fears R, Gogos C, et al. Climate change and human health in the Eastern Mediterranean and Middle East: Literature review, research priorities and policy suggestions. Environ Res. 2023;216:114537.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdul-Wahab S, Bouhamra W, Ettouney H, Sowerby B, Crittenden BD. Analysis of air pollution at Shuaiba Industrial Area in Kuwait. Toxicol Environ Chem. 2000;78(3\u0026ndash;4):213\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGomez R, Summers M, Summers A, Wolf A, Summers J. Depression Anxiety Stress Scales-21: Measurement and Structural Invariance Across Ratings of Men and Women. Assessment. 2014;21(4):418\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZwolinsky S, McKenna J, Pringle A, Widdop P, Griffiths C. Physical activity assessment for public health: efficacious use of the single-item measure. Public Health. 2015;129(12):1630\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVert C, S\u0026aacute;nchez-Benavides G, Mart\u0026iacute;nez D, Gotsens X, Gramunt N, Cirach M, et al. Effect of long-term exposure to air pollution on anxiety and depression in adults: A cross-sectional study. Int J Hyg Environ Health. 2017;220(6):1074\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQiu H, Zhu X, Wang L, Pan J, Pu X, Zeng X, et al. Attributable risk of hospital admissions for overall and specific mental disorders due to particulate matter pollution: A time-series study in Chengdu, China. Environ Res. 2019;170:230\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinault L, Thomson EM, Christidis T, Colman I, Tjepkema M, van Donkelaar A, et al. The association between ambient air pollution concentrations and psychological distress. Health Rep. 2020;31(7):3\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadua J, De Prisco M, Oliva V, Fico G, Vieta E, Fusar-Poli P. Impact of air pollution and climate change on mental health outcomes: an umbrella review of global evidence. World Psychiatry Off J World Psychiatr Assoc WPA. 2024;23(2):244\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi KH, Bae S, Kim S, Kwon HJ. Indoor and outdoor PM2.5 exposure, and anxiety among schoolchildren in Korea: a panel study. Environ Sci Pollut Res. 2020;27(22):27984\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePun VC, Manjourides J, Suh H. Association of Ambient Air Pollution with Depressive and Anxiety Symptoms in Older Adults: Results from the NSHAP Study. Environ Health Perspect. 2017;125(3):342\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAltweck L, Marshall TC, Ferenczi N, Lefringhausen K. Mental health literacy: a cross-cultural approach to knowledge and beliefs about depression, schizophrenia and generalized anxiety disorder. Front Psychol. 2015;6:1272.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeedat S, Scott KM, Angermeyer MC, Berglund P, Bromet EJ, Brugha TS, et al. Cross-national associations between gender and mental disorders in the WHO World Mental Health Surveys. Arch Gen Psychiatry. 2009;66(7):785.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbu-Hamda B, Soliman A, Babekr A, Bellaj T. Emotional Expression and Culture: Implications from Nine Arab Countries. Eur Psychiatry. 2017;41(S1):S230\u0026ndash;230.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFluharty M, Taylor AE, Grabski M, Munaf\u0026ograve; MR. The Association of Cigarette Smoking With Depression and Anxiety: A Systematic Review. Nicotine Tob Res Off J Soc Res Nicotine Tob. 2017;19(1):3\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Department of Health and Human Services. Smoking Cessation: A Report of the Surgeon General (Executive Summary). 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobles TF, Slatcher RB, Trombello JM, McGinn MM. Marital quality and health: A meta-analytic review. Psychol Bull. 2014;140(1):140\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhisman MA. Marital distress and DSM-IV psychiatric disorders in a population-based national survey. J Abnorm Psychol. 2007;116(3):638\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoss BD, Rhoades GK, Stanley SM, Markman HJ. Marital therapy, retreats, and books: The who, what, when, and why of relationship help-seeking. J Marital Fam Ther. 2009;35(1):18\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCacioppo S, Capitanio JP, Cacioppo JT. Toward a Neurology of Loneliness. Psychol Bull. 2014;140(6):1464\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaladchibachi S, Al-Dhafiri AM. Mental health care in Kuwait: Toward a community-based decentralized approach. Int Soc Work. 2018;61(3):329\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoyal M, Singh S, Sibinga EMS, Gould NF, Rowland-Seymour A, Sharma R, et al. Meditation Programs for Psychological Stress and Well-being: A Systematic Review and Meta-analysis. JAMA Intern Med. 2014;174(3):357\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKessler RC. The effects of stressful life events on depression. Annu Rev Psychol. 1997;48:191\u0026ndash;214.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Air pollution, Particulate matter (PM2.5 and PM10), Depression, Anxiety, Kuwait","lastPublishedDoi":"10.21203/rs.3.rs-6465076/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6465076/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background\n\nThe increasing global prevalence of mental health problems such as depression and anxiety has inspired further research into environmental risk factors, including air pollution. Particulate matter (PM), primarily PM2.5 and PM10, has been related to neuroinflammation, hormone changes, and changes in brain structure, all of which may have an effect on mental health. However, data from the Middle East are still sparse.\n\nAim\n\nThis study explored the association between exposure to PM2.5 and PM10 and the prevalence of depression and anxiety among adults living in six urban areas in Kuwait.\n\nMethods\n\nA cross-sectional study was carried out to evaluate mental health using the DASS-21, a validated online questionnaire. PM concentrations were collected during a 22-month period (January 2022 to October 2023). Descriptive statistics, chi-square tests, and multivariable logistic regression were used to investigate relationships after adjusting for demographic and lifestyle characteristics.\n\nResults\n\nAmong the 640 individuals, 82.8% reported depression and 87.3% anxiety. The risk of depression was considerably raised by exposure to harmful levels of PM2.5 and PM10 (OR = 1.7 and 2.9, respectively). The odds of depression were higher for men (OR = 2.8) and married people (OR = 2.2). Living alone or with others raised the risk of anxiety (OR = 3.3) and depression (OR = 3.1). The risk for both outcomes was doubled by stressful life events. Self-employed or part-time workers (OR = 5.3) and smokers (OR = 4.5) were more likely to experience anxiety.\n\nConclusion\n\nThis study focuses on the mental health concerns posed by air pollution in Kuwait. It is crucial to address air quality through public health integration, urban planning, and environmental policy. To demonstrate causation and targeted interventions, longitudinal research is needed.","manuscriptTitle":"Depression and Anxiety among Adults in Kuwait: A Cross-Sectional Study on the Role of Air Pollution","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 19:08:36","doi":"10.21203/rs.3.rs-6465076/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"faff65bb-ce01-44ee-b7b4-8a58b0a8e2ab","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T02:37:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 19:08:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6465076","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6465076","identity":"rs-6465076","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}