Risk factors for multimorbidity in adolescents and young adults in Brazil

Risk factors for multimorbidity in adolescents and young adults in Brazil | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Risk factors for multimorbidity in adolescents and young adults in Brazil Pedro Olivares-Tirado, Rosendo Zanga, Julieta Aránguiz-Ramírez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7368586/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Feb, 2026 Read the published version in Discover Public Health → Version 1 posted 11 You are reading this latest preprint version Abstract Background Recent studies have highlighted the growing trend of multiple long-term conditions (MLTC) in younger people due to unhealthy lifestyles and various environmental stressors. This study aimed to investigate the effects of demographic, socioeconomic, health, and lifestyle factors on MLTC risk in Brazilian youth, with a particular focus on sex differences. Methods Using a cross-sectional approach, this study used data from the 2019 Brazilian National Health Survey to analyze MLTC in people aged 15–29 years. It examines 14 self-reported chronic conditions and independent variables, such as sociodemographic, health and lifestyle behaviors. Multivariate logistic regressions were used to identify factors and average marginal effects were employed to estimate the risk of multimorbidity. Results According to a survey of 17,708 young people, the overall MLTC prevalence was 8.02%, which was greater than that in young women (9.7%). Depression and mental disorders linked to asthma, hypercholesterolemia and hypertension were common. The key predictor factors included self-perceived health, self-esteem, and sleep disturbances. Regional socioeconomic disparities can mask problems of under- and overdiagnosis of the diseases analyzed. Education and physical activity were not significant factors in this study. Conclusion Increasing rates of multimorbidity in young people pose significant challenges for healthcare systems and society. Chronic conditions such as mixed depressive and anxiety disorders, along with physical-mental comorbidities, can result in psychosocial issues, health risks, poor quality of life, and premature death. Therefore, it is essential to gain a better understanding of MLTC in youth to effectively prevent chronic diseases early in life, particularly in LMICs. Adolescent Young adult Multimorbidity Multiple long-term conditions Gender differences Brazil Introduction Multimorbidity, also known as Multiple Long-term Conditions (MLTCs), is a major global issue affecting individuals, families, and society [ 1 , 2 ]. The WHO defines multimorbidity as having two or more chronic health conditions in one individual [ 3 ]. However, the term has been redefined as "multiple long-term conditions"(MLTC) by the National Institute for Health and Care Research (NIHR, UK) to address concerns about negative connotations for patients and narrow biomedical care approaches [ 4 – 6 ]. MLTC was defined as having two or more long-term conditions, including physical and mental health conditions or single conditions with multiple system impacts [ 5 ]. There is clear evidence of a link between MLTCs and age, sex and socioeconomic status [ 7 – 9 ]. Multiple long-term conditions are strongly associated with age, with a higher prevalence in older people but a rapid increase in middle-aged people [ 2 , 8 , 9 ]. The longevity population, which in part reflects improvements in survival from acute and chronic conditions; however, other risk factors, such as income inequality and poverty; and changes in lifestyles, i.e., physical inactivity and sedentary or unhealthy diets, seem to be consistently associated with an increased risk of MLTCs at earlier ages [ 2 , 10 – 12 ]. The prevalence of multiple long-term conditions is increasing, and these conditions are more prevalent in older people, women and socioeconomically disadvantaged groups across the wealthiest countries [ 1 , 12 , 13 ]. Recent studies have shown that the overall global prevalence of multimorbidity in the adult population fluctuates between 30 and 50%, depending on methodological factors [ 2 , 13 , 14 ]. A recent systematic review and meta-analysis revealed that the overall global prevalence of MLTC was 37.2%, and that of South America was 45.7%, the highest prevalence of multimorbidity worldwide [ 14 ]. Individuals with multiple morbidities face a higher risk of hospital admission disability, worse quality of life and premature death. They also often grapple with functional and/or mental health issues, polypharmacy, and increasing economic costs [ 2 , 15 , 16 ]. Moreover, the MLTC significantly impacts the healthcare system by increasing the utilization of healthcare services [ 1 , 12 ] and has a significant impact on work productivity, absenteeism and presenteeism rates, particularly in young workers [ 17 ]. Based on National Health Survey data (2019), studies in Brazil showed that the overall prevalence of MLTC in adults was 29.5%, with 35.9% in women and 22.1% in men. The MLTC rate increases with age from 18.7–29.9% from 2013 to 2019 [ 9 , 18 , 19 ]. Furthermore, the overall prevalence of MLTC in the young adult population (18–29) in Brazil was approximately 9.0% [ 18 , 19 ]. Research on multiple long-term conditions is recognized as an urgent global priority. However, few studies have focused on multiple morbidities in adolescents and young adults, creating a gap in the scientific evidence [ 17 , 20 , 21 ]. Individuals in this age group, often perceived as relatively healthy, are experiencing increasing incidences of chronic diseases such as obesity, diabetes, asthma, depression, and anxiety [ 20 , 21 ], particularly in low- and middle-income countries (LMICs), where chronic diseases reach the levels observed in high-income countries (HICs) [ 22 – 25 ]. Adolescence and early adulthood are critical periods during which young people adopt various lifestyle behaviors that significantly impact their long-term health outcomes and overall well-being. Unhealthy lifestyle behaviors, including poor diet, physical inactivity, smoking, substance use, and environmental stressors, are major contributors to multimorbidity risk in youth. These behaviors often cluster together and are influenced by peer norms, media exposure, and sociocultural environments [ 26 , 27 ]. Furthermore, research has consistently shown that people in the lowest income brackets who live in the most deprived areas in HICs have a greater risk of multimorbidity [ 28 , 29 ]. Furthermore, studies across LMICs have shown inconsistent associations between income and the MLTC, reporting an increasing risk of MLTC with increasing income [ 28 ]. Understanding the factors that contribute to multimorbidity among young people is essential, particularly for vulnerable groups in LMICs. This understanding is vital for achieving Sustainable Development Goal 3.4 (SDG 3.4), which aims to reduce premature deaths from no communicable diseases (NCDs) by one-third through prevention and treatment by 2030 [ 14 , 15 ]. This study aimed to investigate the associations between individual demographic, socioeconomic, health, and lifestyle factors and the risk of multiple long-term conditions in Brazilian adolescents and early adults. This study will focus on gender differences and provide empirical evidence to inform public health policies aimed at promoting early intervention and preventing chronic diseases among at-risk young individuals. Method Data and sample population This observational cross-sectional study is based on the Brazilian National Health Survey 2019 (NHS-2019), which was conducted by the Brazilian Institute of Geography and Statistics (IBGE) in collaboration with the Ministry of Health. The survey represents Brazil's noninstitutionalized population and covers national, regional, state, and metropolitan levels. The sample was drawn from an IBGE master sample, which was stratified into three cluster stages: census tracts chosen with proportional probability, randomly selected households, and individuals aged 15 or older chosen from each household. Interviews were conducted between August 2019 and March 2020 using smartphones programmed with the survey questionnaire. A total of 90,846 households and 275,323 individuals participated, for a total response rate of 93.6% [30]. This study focused on participants aged 15 to 29 who were capable of responding independently and provided complete information for the relevant variables, resulting in a sample of 17,708 individuals. Dependent variable Multimorbidity or MLTC was assessed by the number of self-reported physician diagnoses of 14 chronic diseases. These conditions include hypertension, diabetes mellitus, hypercholesterolemia, heart problems, stroke, asthma or wheezing, arthritis, work-related musculoskeletal disorders (WMSD), depression, other mental health disorders, chronic obstructive pulmonary disease (COPD), cancer, chronic kidney failure and other chronic diseases. Participants reported their diagnoses in response to the question “Has any doctor ever diagnosed you with...? For depression, the question was “Has any doctor or mental health professional ever diagnosed you with depression?” Responses were yes or no. MLTC was defined as having at least two of the fourteen selected conditions mentioned above and was categorized as a dichotomous variable (1 = multiple conditions; 0 = none or one chronic disease). Independent variables Individual factors The final models included factors such as age, sex, ethnicity, self-esteem, and lack of interest or anhedonia. Participants were divided into age groups: 15-19 years (reference), 20-24 years, and 25-29 years. Gender was a binary variable, with men serving as the reference. The ethnicity categories included white, black, brown-skinned, yellow, and indigenous, with yellow and indigenous combined as the reference due to sample size. Self-esteem was measured with the question "In the last two weeks, how often have you felt bad about yourself or let your family down?" Lack of interest and anhedonia were assessed with the following item: "In the last two weeks, how often did you have little interest or no pleasure in doing things?" The responses ranged from "none of the days" to "almost every day." The responses were grouped into three categories: "no days", "moderate" (less than half the days), and "severe" (more than half the days), with "no days" serving as the reference group. Geographic and sociodemographic factors The final models included region, urban residence, education level, income deciles, work situation, and current occupation. Brazil is divided into five regions—North, Northeast, Central-West, Southeast, and South—using the South as the reference region due to its greater socioeconomic development. Residence was classified as urban or rural, with rural as the reference. Education is divided into three categories: illiterate/elementary, high school, and graduate, with graduate education serving as the reference. Household income deciles reflect the gross monthly income of employed members, excluding pensioners and domestic employees. The work situation was binary, with individuals not working during the reference week serving as the reference group. Current occupation is grouped into five categories: no occupation, elementary occupation, skilled worker, service worker, and professional/manager, with professionals/managers serving as the reference group. Health and lifestyle risk factors Health insurance was categorized as a dichotomous variable, with those lacking coverage serving as the reference group. Self-rated health was divided into three groups: good/very good, fair, and bad/very bad, with good/very good serving as the reference. According to WHO guidelines, adults should engage in 150 to 300 minutes of moderate-intensity or 75 to 150 minutes of vigorous-intensity aerobic activity each week [31]. Physical activity was assessed through questions about days spent on sports or recreational activities and the daily time spent on these activities, resulting in three categories: physically inactive (0 to 150 min/week), recommended (150 to 300 min/week), and over trained (more than 300 min/week), with the recommended category serving as the reference. Sedentary behavior was measured by the time spent watching television and using electronic devices at home, categorized as little time (less than 2 hr./day), moderate time (2 to 6 hr./day), or much time (6 hr. or more), with the little time group serving as the reference. Sleep disturbances over the past two weeks were categorized as no days, moderate (approximately half the days), or severe (almost daily), with "no days" as the reference group. The survey questionnaire assessed the previous day's consumption of 12 natural and 10 ultra-processed foods. An unhealthy diet was defined by the proportion of ultra-processed foods classified as mild (20% or less), moderate (20%–50%), or severe (over 50%), with mild status serving as the reference. Tobacco and alcohol use were included as dichotomous variables (1 = yes; 0 = no). Statistical analysis Descriptive statistics were used to analyze the geographic, sociodemographic, health, and lifestyle characteristics of young individuals in Brazil, focusing on sex and multimorbidity. The sample design was incorporated into the analysis, enabling valid inferences for the population. The analysis employed the svyset and svy commands to incorporate the primary sampling unit, individual weights, and strata. Multicollinearity was assessed using correlation matrices and variance inflation factors (VIFs). Finally, multivariate logistic regression models were constructed to examine multiple long-term conditions by sex using both raw and survey data sets. Model specification Univariate analysis was initially performed for each variable, focusing on those with p values less than 0.25 based on the Wald test from logistic regression. A purposeful approach was then used to refine covariate selection [32]. Variables with significance levels of 0.1 or 0.15 were included, while no significant covariates not considered confounders were removed. Odds ratios (ORs), standard errors, and 95% confidence intervals were estimated. Model goodness-of-fit was assessed using the Hosmer–Lemeshow test, and predictive ability was evaluated with the area under the curve (AUC). Average marginal effects (AMEs) were calculated using the dx/dy command and the delta method for standard errors to show how predicted probabilities for multimorbidity change as predictors shift from the reference to the interest category while controlling for the other variables [33]. Statistical significance was determined using Wald's chi-square test, accepting a 10% significance level due to the exploratory nature of the study, with analyses conducted using STATA version 14.0 (StataCorp, TX, USA). Results Descriptive In 2019, 17,708 young people aged 15 to 29 years were interviewed, with an average age of 22.9 years (SD=4.15). In the survey sample, 8.02% (95% CI: 7.72; 8.32) reported multiple long-term conditions (MLTC), which was slightly greater than the 7.23% reported in the raw data. Most individuals with MLTC were women (61.0%), with an average age of 23.6 years (SD: 4.01) and a prevalence of 9.7% (95% CI: 9.2; 10.2). In men, the prevalence was 6.3% (95% CI: 5.9; 6.7). Prevalence varied by age: 6.3% (95% CI: 5.9; 6.8) for 15-19-year-olds, 9.0% (95% CI: 8.5; 9.5) for 20-24-year-olds, and 8.8% (95% CI: 8.2; 9.4) for those aged 25-29 years. Table 1 details the sample characteristics related to sex and MLTC among Brazilian youth. Table 1: Sample characteristics according to sex and multimorbidity status in young Brazilian people. NHS-2019 Characteristics Women Men non-MLTC (n:8,379) MLTC (n:872) non-MLTC (n:8,048) MLTC (n:409) Age groups (n,%) *** 15-19-year-olds (ref.) 2,059 (24.6%) 154 (17.7%) 2,040 (25.3%) 82 (20.0%) 20-24-year-olds 2,930 (35.0%) 316 (36.2%) 2,751 (34.2%) 127 (31.1%) 25-29-year-olds 3,390 (40.5%) 402 (46.1%) 3,257 (40.5%) 200 (48.9%) Ethnicity (n,%)*** white (ref.) 2,492 (29.7%) 318 (36.5%) 2,458 (30.5%) 169 (41.3%) black 882 (10.5%) 115 (13.2%) 978 (12.2%) 47 (11.5%) brown-skinned 4,880 (58.2%) 423 (48.5%) 4,493 (55.8%) 187 (45.7%) others(yellow, indigenous) 125 (1.5%) 16 (1.8%) 119 (1.5%) 6 (1.5%) Education levels (n,%) *** Illiterate/elementary school 2,161 (25.8%) 196 (22.5%) 2,565 (31.9%) 106 (25.9%) high school 4,376 (52.2%) 432 (49.5%) 4,084 (50.7%) 189 (46.2%) graduated (ref.) 1,842 (22.0%) 244 (28.0%) 1,399 (17.4%) 114 (27.9%) Urban residence (n, %) *** 6,482 (77.4%) 725 (83.1%) 5,959 (74.0%) 346 (84.6%) Region (n,%) *** north 2,102 (25.1%) 160 (18.3%) 2,046 (25.4%) 71 (17.4%) northeast 3,067 (36.6%) 257 (29.5%) 2,854 (35.5%) 114 (27.9%) central west 997 (11.9%) 103 (11.8%) 921 (11.4%) 46 (11.2%) southeast 1,421 (17.0%) 216 (24.8%) 1,428 (17.7%0 109 (26.7%) south (ref.) 792 (9.5%) 136 (15.6%) 799 (9.9%) 69 (16.9%) Working (n,%) ** 3,428 (40.9%) 423 (48.5%) 5,069 (63.0%) 235 (57.5%) Working Occupation *** no occupation 4,539 (54.2%) 405 (46.4%) 2,484 (30.9%) 156 (38.1%) elementary 596 (7.1%) 68 (7.8%) 921 (11.4%) 75 (18.3%) qualified, skilled and artisans 348 (4.2%) 41 (4.7%) 1,386 (17.2%) 77 (18.8) administrative support and services 2,093 (25.0%) 233 (26.7%) 1,968 (24.5%) 64 (15.6%) Professionals and technicians (ref.) 803 (9.6%) 125 (14.3%) 1,289 (16.0%) 37 (9.0%) Household income deciles (n,%) *** 1st decile 1,310 (15.9%) 107 (12.5%) 963 (12.2%) 27 (6.6%) 2nd decile 1,185 (14.4%) 92 (10.7%) 995 (12.6%) 45 (11.0%) 3rd decile 965 (11.7%) 98 (11.4%) 842 (10.6%) 39 (9.6%) 4th decile 846 (10.3%) 76 (8.9%) 831 (10.5%) 40 (9.8%) 5th decile 767 (9.3%) 78 (9.1%) 833 (10.5%) 46 (11.3%) 6th decile 741 (9.0%) 72 (8.4%) 730 (9.2%) 28 (6.9%) 7th decile 732 (8.9%) 93 (10.9%) 782 (9.9%) 40 (9.8%) 8th decile 648 (7.9%) 84 (9.8%) 741 (9.4%) 43 (10.5%) 9th decile 569 (6.9%) 75 (8.8%) 645 (8.1%) 47 (11.5%) 10th decile 473 (5.7%) 82 (9.6%) 556 (7.0%) 53 (13.0%) Health insurance(n,%) *** 1,351 (16.1%) 233 (26.7%) 1,349 (16.8%) 117 (28.6%) Self-rated health status *** good/very good (ref.) 6,763 (80.7%) 518 (59.4%) 6,909 (85.8%) 281 (68.7%) fair 1,508 (18.0%) 295 (33.8%) 1,076 (13.4%) 101 (24.7%) bad/very bad 108 (1.3%) 59 (6.8%) 63 (0.8%) 27 (6.6%) Lack of interest or a nhedonia *** no day (ref.) 5,481 (65.4%) 332(38.1%) 6,236 (77.5%) 226 (55.3%) moderate (less than half of days) 1991 (23.8%) 268 (30.7%) 1,398 (17.4%) 109 (26.7%) severe (almost every day) 907 (10.8%) 272 (31.2%) 414 (5.1%) 74 (18.1%) Self-esteem problems *** no day (ref.) 6,628 (79.1%) 423 (48.5%) 7,084 (88.0%) 262 (64.1%) moderate (less than half of days) 1117 (13.3%) 196 (22.5%) 694 (8.6%) 87 (21.3%) severe (almost every day) 634 (7.6%) 253 (29.0%) 270 (3.4%) 60 (14.7%) Physical activity the last week (n,%) ** inactive(300 min/week) 878 (10.5%) 120 (13.8%) 1,656 (20.6%) 85 (20.8%) Sitting time watching television (n,%) ** little time (do not - < 2 hours/day) (ref.) 4,876 (58.2%) 545 (62.5%) 5,079 (63.1%) 277 (67.7%) moderate time (2 hours - 6 hours/day) 549 (6.6%) 53 (6.1%) 336 (4.2%) 23 (5.6%) Sitting time using computer (n,%) ** little time (do not - <2 hours/day) (ref.) 3,082 (36.8%) 259 (29.7%) 3,118 (38.7%) 131 (32.0%) moderate time (1 hour - 6 hours/day) 1,798 (21.5%) 233 (26.7%) 1,575 (19.6%) 94 (23.0%) Sleep disturbances (n,%)*** no day (ref.) 5,820 (69.5%) 323 (37.0%) 6,380 (79.3%) 200 (48.9%) moderate(around half of the days) 1,479 (17.7%0 225 (25.8%) 1,032 (12.8%) 99 (24.2%) severe (almost every day) 1,080 (12.9%) 324 (37.2%) 636 (7.9%) 110 (26.9%) Consumpt. of ultra-processed foods ** mild ( 20% to = 50%) 1,494 (17.9%) 168 (19.4%) 1,370 (17.1%) 64 (15.8%) Smoker ** 446 (5.3%) 88 (10.1%) 1,178 (14.6%) 83 (20.3%) Alcohol consumption *** 2,945 (35.1%) 369 (42.3%) 4,223 (52.5%) 220 (53.8%) MLTC : Multiple Long-term Conditions or Multimorbidity. ( ref ): reference group ⁿᶳ : no significant; * : p < 0.1; ** : p < 0.05; *** : p < 0.0001 The main prevalence rates of chronic diseases in the sample were as follows: asthma (6.3%), other mental disorders (5.6%), depression (5.2%), other chronic diseases (4.7%), hypertension (4.2%), and hypercholesterolemia (3.7%). Both depression and other mental disorders accounted for 44% of the multimorbidity cases, while asthma contributed 32.2%. In terms of multimorbidity status among young men, 79.7% reported having two conditions, 15.2% had three, and 5.1% had four or more. The most common combinations for those with two conditions included depression and other mental disorders (19.9%), asthma with other chronic diseases (6.7%) and asthma and other mental disorders (5.5%). For those with three or more conditions, notable combinations included depression, other mental disorders, and other chronic diseases (14.5%); asthma, depression, and other mental disorders (11.3%); and asthma, hypertension and hypercholesterolemia (4.8%). Among young women, 70.8% had two chronic conditions, 22.0% had three, and 7.2% had four or more. Common combinations for those with two conditions included depression and other mental disorders (21.2%), hypertension and hypercholesterolemia (5.2%), and asthma with depression (4.5%). For women with three or more conditions, notable combinations included asthma, depression and other mental disorders (9.9%), depression, hypercholesterolemia and other mental disorders (7.3%), and depression, other mental disorders and other chronic diseases (7.3%). Model Goodness of Fit Statistics The multicollinearity diagnostic revealed that most variables exhibited low correlations (r < .30), except for the following pairs: work situation and current occupation, self-esteem and anhedonia, sleep disturbances and anhedonia, deciles and health insurance, and deciles and education levels, which showed moderate correlations. The mean variance inflation factor (VIF) was low at 1.36, indicating no multicollinearity in the models. Goodness-of-fit tests confirmed the model's significance, and the area under the ROC curve (AUC) showed moderate predictive power. Multiple long -term conditions in the overall sample. The analysis of the raw data model identified several key predictors of multimorbidity among young people in Brazil. These included being female, aged >20-29 years, from higher income groups, rating health as fair or poor, having health insurance, experiencing anhedonia and low self-esteem, prolonged computer use, sleep issues, consuming ultra-processed foods, and smoking. Protective factors included living in specific regions, watching moderate television, holding elementary or skilled jobs, and consuming alcohol. Notably, education level and physical activity were not significantly related. The survey data model identified fewer significant predictors of multimorbidity than did the raw data model, with some moderate changes in magnitude and significance. Key predictors included age >20-29 years, a rating of health as fair or poor, severe lack of interest or anhedonia, and moderate to severe self-esteem. Additional factors included moderate time spent on computer use at home, sleep disturbances, having health insurance, higher socioeconomic status, and moderate consumption of ultra-processed foods, although their p values were approximately 0.10. The protective factors identified include living in the northern, northeastern, or central western regions; holding skilled jobs; and consuming moderate amounts of alcohol. However, gender, ethnicity, education level, physical activity, and television viewing time were not significantly related to the participants. Table 2 shows the odds ratios (ORs) and average marginal effects (AMEs) with 95% confidence intervals (CIs) from the survey data logistic regression model examining multimorbidity in the overall sample of young Brazilian people. Table 2. Predictors and Marginal Effects of Multimorbidity from survey data of the overall sample . Covariates Logistic Regression Average Marginal Effects OR Std. Error [95% C.I.) dy/dx Std. Error [95% C.I.) Age groups 20-24-year-olds 1.661 ** 0.2786 (1.196 - 2.308) 0.031 ** 0.0102 (0.011 - 0.051) 25-29-year-olds 1.560 ** 0.2501 (1.140 - 2.137) 0.027 ** 0.0093 (0.008 - 0.045) Women 1.048 ⁿᶳ 0.147 (0.796 - 1.380) 0.003 ⁿᶳ 0.0089 (-0.014 - 0.020) Ethnicity black 1.082 ⁿᶳ 0.2022 (0.750 - 1.561) 0.005 ⁿᶳ 0.0127 (-0.020 - 0.030) brown-skinned 0.917 ⁿᶳ 0.1198 (0.710 - 1.185) -0.005 ⁿᶳ 0.0083 (-0.022 - 0.011) others(yellow, indigenous) 0.787 ⁿᶳ 0.3103 (0.364 - 1.705) -0.014 ⁿᶳ 0.0221 (-0.058 - 0.029) Education levels Illiterate/elementary school 0.922 ⁿᶳ 0.1789 (0.630 - 1.349) -0.006 ⁿᶳ 0.0133 (-0.031 - 0.020) high school 0.805 ⁿᶳ 0.133 (0.582 - 1.113) -0.014 ⁿᶳ 0.0112 (-0.036 - 0.008) Urban residence 0.943 ⁿᶳ 0.1356 (0.711 - 1.250) -0.004 ⁿᶳ 0.0095 (-0.022 - 0.015) Region north 0.458 *** 0.0825 (0.321 - 0.652) -0.051 *** 0.012 (-0.074 - -0.027) northeast 0.463 *** 0.0775 (0.334 - 0.643) -0.050 *** 0.0115 (-0.073 - -0.028) central west 0.560 ** 0.104 (0.389 - 0.806) -0.040 ** 0.0127 (-0.065 - -0.016) southeast 0.797 ⁿᶳ 0.1172 (0.598 - 1.063) -0.018 ⁿᶳ 0.0116 (-0.040 - 0.005) Working 1.064 ⁿᶳ 0.2438 (0.679 - 1.667) 0.004 ⁿᶳ 0.0147 (-0.025 - 0.033) Working occupation no occupation 0.930 ⁿᶳ 0.2673 (0.529 - 1.634) -0.005 ⁿᶳ 0.0207 (-0.046 - 0.035) elementary 0.646 ⁿᶳ 0.1834 (0.370 - 1.127) -0.028 ⁿᶳ 0.0180 (-0.063 - 0.007) qualified, skilled and artisans 0.520 ** 0.1275 (0.321 - 0.841) -0.039 ** 0.0148 (-0.068 - -0.010) administrative and services 0.737 ⁿᶳ 0.1436 (0.503 - 1.080) -0.020 ⁿᶳ 0.0136 (-0.047 - 0.006) Household income deciles 1.062 * 0.0293 (1.006 - 1.121) 0.004 ** 0.0018 (0.000 - 0.007) health insurance 1.278 * 0.1836 (0.964 - 1.694) 0.016 * 0.0099 (-0.003 - 0.036) Self-rated health status fair 2.736 *** 0.3535 (2.124 - 3.524) 0.081 *** 0.0126 (0.056 - 0.106) bad/very bad 7.805 *** 2104 (4.601 - 1.324) 0.230 *** 0.0461 (0.139 - 0.320) Lack of interest/anhedonia moderate 1.161 ⁿᶳ 0.1779 (0.860 - 1.568) 0.010 ⁿᶳ 0.0099 (-0.010 - 0.029) severe 1.402 ** 0.233 (1.012 - 1.942) 0.023 ** 0.0119 (0.000 - 0.046) Self-esteem problems moderate 1.799 ** 0.3439 (1.236 - 2.616) 0.040 ** 0.015 (0.011 - 0.069) severe 3.350 *** 0.5782 (2.388 - 4.699) 0.104 *** 0.0193 (0.066 - 0.141) Physical activity inactive 0.909 ⁿᶳ 0.1355 (0.679 - 1.218) -0.006 ⁿᶳ 0.0098 (-0.025 - 0.013) over-trained 0.983 ⁿᶳ 0.1959 (0.665 - 1.453) -0.001 ⁿᶳ 0.0132 (-0.027 - 0.025) Sitting time watching TV moderate time 0.915 ⁿᶳ 0.1152 (0.714 - 1.171) -0.006 ⁿᶳ 0.0078 (-0.021 - 0.010) many time 1.442 ⁿᶳ 0.3681 (0.874 - 2.378) 0.027 ⁿᶳ 0.0205 (-0.014 - 0.067) Sitting time using computer moderate time 1.427 ** 0.1943 (1.093 - 1.864) 0.022 ** 0.0086 (0.006 - 0.039) many time 1.142 ⁿᶳ 0.1768 (0.843 - 1.547) 0.008 ⁿᶳ 0.009 (-0.010 - 0.025) Sleep disturbances moderate 1.755 *** 0.2753 (1.291 - 2.387) 0.035 ** 0.0108 (0.014 - 0.057) severe 3.282 *** 0.5238 (2.401 - 4.488) 0.095 *** 0.0161 (0.064 - 0.127) Ultra-processed foods intake moderate 1.268 * 0.1677 (0.978 - 1.643) 0.015 * 0.0079 (-0.001 - 0.030) high 1.215 ⁿᶳ 0.218 (0.855 - 1.727) 0.012 ⁿᶳ 0.011 (-0.010 - 0.033) Smoker 0.989 ⁿᶳ 0.2015 (0.663 - 1.475) -0.001 ⁿᶳ 0.0129 (-0.026 - 0.025) Alcohol use 0.760 ** 0.0909 (0.602 - 0.961) -0.017 ** 0.0076 (-0.032 - -0.002) constant 0.029 *** 0.0125 (0.012 - 0.067) - - - dy/dx : the marginal change of the outcome variable(dy) concerning the change from the reference group of the factors(dx). ⁿᶳ : no significant; * : p < 0.1; ** : p < 0.05; *** : p < 0.0001 Young individuals in poor or fair health are significantly more likely to experience multimorbidity, with rates of 23.0% and 8.1%, respectively, than are those in good or very good health. Those with severe self-esteem issues were 10.4% more likely to have multiple morbidities, while those with moderate self-esteem issues were 4.0% more likely. Severe sleep disturbances increase the likelihood of multimorbidity by 9.5%. Individuals aged >20-24 years and >20-29 years with health insurance and higher incomes had a less than 3.0% increased likelihood of multimorbidity, while those in the northern and central western regions were less likely to experience it than their peers in the southern region were. Additionally, individuals in skilled or artisan jobs are 3.9% less likely to suffer from multimorbidity than professional workers are, and alcohol consumers are 1.8% less likely to be affected than no consumers are. Multiple long -term conditions in young women The analysis of women's raw data revealed key predictors of multimorbidity, including age >20-29 years, having health insurance, having a higher income, having poor self-rated health, lacking interest or anhedonia, self-esteem issues, high computer use, sleep disturbances, moderate consumption of ultra-processed foods, and smoking. Protective factors included being brown-skinned, living in northern or central western regions, physical inactivity compared to those who met activity recommendations, and alcohol consumption. Education levels, television viewing time, and occupational activities were not significantly different. Table 3 presents the odds ratios (ORs) and average marginal effects (AMEs) with 95% confidence intervals (95% CIs) from the survey data logistic regression model on multimorbidity among young Brazilian women. Table 3. Predictors and Marginal Effects of Multimorbidity from survey data in young women. Logistic Regression Average Marginal Effects OR Std. Error [95% C.I.) dy/dx Std. Error [95% C.I.) Age groups 20-24-year-olds 1.280 ⁿᶳ 0.2431 (0.882 - 1.857) 0.017 ⁿᶳ 0.013 (-0.008 - 0.043) 25-29-year-olds 1.516 ** 0.2919 (1.039 - 2.211) 0.031 ** 0.0137 (0.004 - 0.058) Ethnicity black 1009 ⁿᶳ. 0.2579 (0.612 - 1.665) 0.001 ⁿᶳ 0.0202 (-0.039 - 0.040) brown-skinned 0.810 ⁿᶳ 0.1221 (0.602 - 1.088) -0.016 ⁿᶳ 0.0112 (-0.038 - 0.006) others(yellow, indigenous) 0.887 ⁿᶳ 0.4089 (0.360 - 2.190) -0.009 ⁿᶳ 0.0338 (-0.075 - 0.057) Education levels Illiterate/elementary school 1.024 ⁿᶳ 0.2348 (0.653 - 1.605) 0.002 ⁿᶳ 0.0183 (-0.034 - 0.038) high school 0.819 ⁿᶳ 0.1488 (0.574 - 1.170) -0.015 ⁿᶳ 0.0140 (-0.042 - 0.013) Urban residence 0.795 ⁿᶳ 0.1366 (0.568 - 1.113) ;-0.018 ⁿᶳ 0.0141 (-0.022 - 0.015) Region north 0.585 ** 0.1269 (0.383 - 0.895) -0.046 ** 0.0189 (-0.083 - -0.009) northeast 0.475 *** 0.0978 (0.317 - 0.711) -0.061 *** 0.0174 (-0.095 - -0.027) central west 0.484 ** 0.1241 (0.293 - 0.800) -0.059 ** 0.02 (-0.099 - -0.020) southeast 0.596 ** 0.1124 (0.412 - 0.862) -0.045 ** 0.0171 (-0.079 -- 0.011) Working 1.305 ⁿᶳ 0.3811 (0.737 - 2.314) 0.020 ⁿᶳ 0.0222 (-0.023 - 0.063) Working occupation no occupation 1.142 ⁿᶳ 0.4067 (0.568 - 2.295) 0.010 ⁿᶳ 0.0268 (-0.042 - 0.063) elementary 0.831 ⁿᶳ 0.2711 (0.438 - 1.575) -0.013 ⁿᶳ 0.0222 (-0.056 - 0.031) qualified, skilled and artisans 1.262 ⁿᶳ 0.4187 (0.659 - 2.419) 0.018 ⁿᶳ 0.0264 (-0.034 - 0.070) administrative and services 0.912 ⁿᶳ 0.2242 (0.563 - 1.476) -0.007 ⁿᶳ 0.0176 (-0.041 - 0.028) Household income deciles 1.082 ** 0.0349 (1.015 - 1.152) 0.006 ** 0.0024 (0.001 - 0.011) health insurance(n,%) 1.610 ** 0.2653 (1.165 - 2.224) 0.038 ** 0.0141 (0.010 - 0.066) Self-rated health status fair 2.899 *** 0.4507 (2.137 - 3.932) 0.097 *** 0.0168 (0.064 - 0.130) bad/very bad 9.546 *** 31.054 (5.045 - 18.064) 0.284 *** 0.0589 (0.169 - 0.400) Lack of interest/anhedonia moderate 1.162 ⁿᶳ 0.1962 (0.834 - 1.618) 0.011 ⁿᶳ 0.0125 (-0.013 - 0.035) severe 1.369 ⁿᶳ 0.2701 (0.929 - 2.015) 0.024 ⁿᶳ 0.0158 (-0.007 - 0.055) Self-esteem problems moderate 1.606 *** 0.2855 (1.134 - 2.276) 0.036 ** 0.0144 (0.007 - 0.064) severe 3.005 *** 0.5471 (2.103 - 4.294) 0.102 *** 0.0205 (0.062 - 0.142) Physical activity inactive 0.838 ⁿᶳ 0.1385 (0.606 - 1.159) -0.014 ⁿᶳ 0.0131 (-0.039 - 0.012) over-trained 0.819 ⁿᶳ 0.1957 (0.513 - 1.309) -0.015 ⁿᶳ 0.0179 (-0.050 - 0.020) Sitting time watching TV moderate time 1.094 ⁿᶳ 0.161 (0.820 - 1.460) 0.007 ⁿᶳ 0.0111 (-0.015 - 0.028) many time 1.245 ⁿᶳ 0.4484 (0.614 - 2.522) 0.017 ⁿᶳ 0.0298 (-0.041 - 0.075) Sitting time using computer moderate time 1.487 ** 0.2598 (1.056 - 2.094) 0.028 ** 0.0121 (0.004 - 0.052) many time 1.451 ** 0.27 (1.007 - 2.090) 0.026 ** 0.0131 (0.00005 - 0.052) Sleep disturbances moderate 2.125 *** 0.4312 (1.428 - 3.164) 0.054 ** 0.0169 (0.021 - 0.087) severe 3.924 *** 0.6979 (2.769 - 5.562) 0.123 *** 0.02 (0.083 - 0.162) Ultra-processed foods intake moderate 1.137 ⁿᶳ 0.1774 (0.838 - 1.544) 0.009 ⁿᶳ 0.0111 (-0.012 - 0.031) high 1.179 ⁿᶳ 0.2543 (0.772 - 1.799) 0.012 ⁿᶳ 0.016 (-0.019 - 0.043) Smoker 1036 ⁿᶳ 0.2564 (0.638 - 1.683) 0.003 ⁿᶳ 0.0188 (-0.034 - 0.039) Alcohol use 0.875 ⁿᶳ 0.1186 (0.670 - 1.141) -0.010 ⁿᶳ 0.0100 (-0.029 - 0.010) constant 0.025 *** 0.013 (0.009 - 0.070) - - - dy/dx : the marginal change of the outcome variable(dy) concerning the change from the reference group of the factors(dx). ⁿᶳ : no significant; * : p < 0.1 ; ** : p < 0.05 ; *** : p < 0.0001 Key predictors of multimorbidity include age 25-29 years, a rating of health as fair or poor, moderate to severe self-esteem issues, and sleep disturbance or spending significant time on computers. Having health insurance and a higher socioeconomic status are also important factors. In contrast, living in the northern, northeastern, central western, or southeastern regions served as protective factors compared to living in the southern region. According to the AMEs from the women's survey data, individuals in poor or fair health are more likely to experience multiple morbidities, with rates of 28.4% and 9.7%, respectively, than are those in good health. Severe sleep disturbances increase the likelihood of multimorbidity by 12.3%, while moderate disturbances increase this likelihood by 5.3%. Similarly, those with severe self-esteem problems are 10.2% more likely to face multimorbidity, and those with moderate self-esteem problems are 3.6% more likely. Compared with their reference group, women aged 25-29 years with health insurance, higher income, and significant computer use had a 3.0% increased risk. Conversely, young women from the northern, northeastern, central western, and southeastern regions are less likely (4.5% - 6.0%) to experience multimorbidity than are those from the southern region. Multiple long -term conditions in young men The analysis of the raw data from the men revealed several key predictors of multimorbidity, including age 25-29 years, having health insurance, rating health as fair or poor, experiencing anhedonia, having self-esteem issues, physical inactivity, and sleep disturbances. Protective factors included living in the northern, northeastern, or central western regions; holding elementary or skilled jobs; and spending moderate amounts of time watching television. Education levels, time spent using computers at home, ultra-processed food consumption, smoking status, and alcohol use were not significantly different. Table 4 presents the odds ratios (ORs) and average marginal effects (AMEs) with 95% confidence intervals (95% CIs) from the survey data logistic regression model of multimorbidity among young Brazilian men. Table 4. Predictors and Marginal Effects of Multimorbidity from survey data in young men. Logistic Regression Average Marginal Effects OR Std. Error [95% C.I.) dy/dx Std. Error [95% C.I.) Age groups 20-24-year-olds 2.415 ** 0.6837 (1.386 - 4.207) 0.044 ** 0.0148 (0.015 - 0.073) 25-29-year-olds 1.769 ** 0.4777 (1.042 - 3.004) 0.025 ** 0.0117 (0.002 - 0.048) Ethnicity black 1.184 ⁿᶳ 0.3243 (0.692 - 2.025) 0.009 ⁿᶳ 0.0152 (-0.021 - 0.039) brown-skinned 1.027 ⁿᶳ 0.2142 (0.683 - 1.546) 0.001 ⁿᶳ 0.0107 (-0.020 - 0.022) others(yellow, indigenous) 0.520 ⁿᶳ 0.3959 (0.117 - 2.313) -0.026 ⁿᶳ 0.0241 (-0.073 - 0.021) Education levels Illiterate/elementary school 0.848 ⁿᶳ 0.2611 (0.463 - 1.550) -0.009 ⁿᶳ 0.0169 (-0.042 - 0.024) high school 0.811 ⁿᶳ 0.2167 (0.480 - 1.369) -0.011 ⁿᶳ 0.0148 (-0.040 - 0.018) Urban residence 1.157 ⁿᶳ 0.3001 (0.696 - 1.924) 0.007 ⁿᶳ 0.0124 (-0.017 - 0.032) Region north 0.303 *** 0.0974 (0.161 - 0.569) -0.050 ** 0.0147 (-0.079 - -0.021) northeast 0.430 ** 0.1248 (0.243 - 0.760) -0.040 ** 0.0149 (-0.069 - -0.010) central west 0.749 ⁿᶳ 0.2586 (0.380 - 1.474) -0.017 ⁿᶳ 0.0192 (-0.054 - 0.021) southeast 1.090 ⁿᶳ 0.2677 (0.673 - 1.764) 0.006 ⁿᶳ 0.0159 (-0.025 - 0.037) Working 0.916 ⁿᶳ 0.3039 (0.478 - 1.756) -0.005 ⁿᶳ 0.0172 (-0.038 - 0.029) Working occupation no occupation 0.912 ⁿᶳ 0.3709 (0.411 - 2.024) -0.006 ⁿᶳ 0.0271 (-0.059 - 0.047) elementary 0.499 * 0.2089 (0.219 - 1.134) -0.038 * 0.0223 (-0.082 - 0.006) qualified, skilled and artisans 0.339 ** 0.1119 (0.178 - 0.648) -0.052 ** 0.0173 (-0.085 - -0.018) administrative and services 0.557 * 0.1691 (0.307 - 1.010) -0.033 * 0.0179 (-0.068 - 0.002) Household income deciles 1.035 ⁿᶳ 0.0456 (0.949 - 1.128) 0.002 ⁿᶳ 0.0023 (-0.003 - 0.006) health insurance(n,%) 0.953 ⁿᶳ 0.2318 (0.592 - 1.535) -0.002 ⁿᶳ 0.0124 (-0.027 - 0.022) Self-rated health status fair 2.499 *** 0.5496 (1.623 - 3.846) 0.060 ** 0.0179 (0.025 - 0.095) bad/very bad 4.907 ** 26.073 (1.732 - 13.3906) 0.133 ** 0.0671 (0.001 - 0.264) Lack of interest/anhedonia moderate 1.147 ⁿᶳ 0.277 (0.714 - 1.841) 0.007 ⁿᶳ 0.0128 (-0.018 - 0.032) severe 1.466 ⁿᶳ 0.4296 (0.825 - 2.604) 0.022 ⁿᶳ 0.0183 (-0.014 - 0.058) Self-esteem problems moderate 2.451 ** 0.8572 (1.234 - 4.865) 0.057 ** 0.0285 (0.001 - 0.113) severe 4.186 *** 12.446 (2.337 - 7.498) 0.111 ** 0.0327 (0.047 - 0.175) Physical activity inactive 1.017 ⁿᶳ 0.2527 (0.625 - 1.655) 0.001 ⁿᶳ 0.0126 (-0.024 - 0.025) over-trained 1.104 ⁿᶳ 0.3241 (0.621 - 1.963) 0.005 ⁿᶳ 0.0153 (-0.025 - 0.035) Sitting time watching TV moderate time 0.688 * 0.1531 (0.445 - 1.065) -0.018 * 0.0101 (-0.038 - 0.002) many time 1.603 ⁿᶳ 0.5378 (0.831 - 3.094) 0.030 ⁿᶳ 0.0243 (-0.017 - 0.078) Sitting time using computer moderate time 1.313 ⁿᶳ 0.2733 (0.873 - 1.975) 0.015 ⁿᶳ 0.0113 (-0.007 - 0.037) many time 0.759 ⁿᶳ 0.2089 (0.442 - 1.302) -0.012 ⁿᶳ 0.0121 (-0.036 - 0.011) Sleep disturbances moderate 1.333 ⁿᶳ 0.2851 (0.876 - 2.027) 0.015 ⁿᶳ 0.0112 (-0.007 - 0.036) severe 2.787 *** 0.7605 (1.632 - 4.758) 0.069 ** 0.0232 (0.023 - 0.114) Ultra-processed foods intake moderate 1.563 * 0.3638 (0.991 - 2.467) 0.021 ** 0.0103 (0.001 - 0.041) high 1.373 ⁿᶳ 0.4155 (0.759 - 2.485) 0.014 ⁿᶳ 0.0139 (-0.013 - 0.042) Smoker 1.043 ⁿᶳ 0.3259 (0.565 - 1.924) 0.002 ⁿᶳ 0.0165 (-0.030 - 0.035) Alcohol use 0.624 ** 0.119 (0.430 - 0.907) -0.025 ** 0.0102 (-0.044 - -0.005) constant 0.028 *** 0.0196 (0.007 - 0.110) - - - dy/dx : the marginal change of the outcome variable(dy) concerning the change from the reference group of the factors(dx). ⁿᶳ : no significant; * : p < 0.1 ; ** : p < 0.05 ; *** : p 20 to 29 years, rating one’s health as fair or poor, having moderate to severe self-esteem issues, and experiencing severe sleep disturbances. Moderate consumption of ultra-processed foods also plays a role. Protective factors include living in the northern, northeastern, central western, or southeastern regions compared to the southern region and working in elementary, skilled, or administrative jobs instead of professional roles. Moderate television viewing and alcohol consumption are also protective. Factors such as ethnicity, education level, physical activity, lack of interest or anhedonia, computer use at home, and smoking were not significantly different. According to the AMEs, individuals in poor or fair health have 13.3% and 6.0% greater chances of having multimorbidity, respectively, than do those in good health. Severe self-esteem increases the likelihood of multimorbidity by 11.1%, while moderate sleep disturbances increase this likelihood by 5.7%. Young men aged 20-24 and 25-29 years had 4.4% and 2.5% greater likelihoods, respectively, than did those in the reference group, and moderate consumption of ultra-processed foods increased this likelihood by 2.1%. Conversely, men from the northern and northeastern regions were 5.0% and 4.0%, respectively, less likely to experience multimorbidity than were those from the southern region. Additionally, individuals in elementary, skilled, or administrative jobs are less likely to have multimorbidity than their professional counterparts are. Individuals who consumed alcohol and watched television moderately were also less likely to experience multimorbidity. Discussion An increasing number of adolescents and early-stage adults are affected by multiple long-term conditions. Most related research has focused on older adults, leaving a gap in understanding of multimorbidity in younger populations. This is crucial, as multimorbidity significantly impact health outcomes and quality of life. This study examined multimorbidity among Brazilian youth aged 15-29 years and reported an overall prevalence of 8.2%, which increases with age and is greater in women. There was a prevalence rate of 9.4% in women aged 15-29 years, nearly double that in men (4.8%). These findings align with recent Brazilian studies that reported similar rates for individuals aged 18-29 [18,19]. Globally, the prevalence of MLTC among the young population varies widely due to differences in chronic condition definitions and age groupings. For instance, a 2020 study in England reported rates of 0.9% for those aged 0-19 and 5.9% for those aged 20-49 [34]. In Ontario, Canada, a 2016 study reported rates of 3.8% for women aged 0-17 years and 17.9% for those aged 18-44 years, while male rates were 5.0% and 14.8%, respectively [35]. Examining 14 chronic diseases, the study revealed that the most common combination of multimorbidity was depression with other mental disorders, affecting approximately 20% of men and women. Anxiety—other mental disorders—often observed in early adulthood—can lead to mixed depressive and anxiety disorder (MDAD), which particularly affects younger individuals and women. The World Health Organization (WHO) recognized MDAD among the ICD-11 codes in 2018, with prevalence rates in primary care ranging from 1.8% to 11% [36,37]. These findings are consistent with previous research showing that depression rates are greater among those aged 14-22 years, especially among young women. Furthermore, depression frequently coexists with anxiety disorders, with a lifetime depression risk of 20% to 70% for these patients. Experiencing both conditions during adolescence can lead to significant stress and consequences such as school dropout or work-related issues [38-40]. The study also revealed a link between depression and chronic diseases such as asthma, high cholesterol, and hypertension in both sexes. This finding aligns with prior research showing that depression often coexists with other conditions, especially when it starts in adolescence or early adulthood [41-43]. These findings underscore the need for public health initiatives to address the mental health crisis among youth. Early symptoms of depression and anxiety are significant risk factors for developing multiple health issues in middle age. Thus, it is crucial to consider these mental disorders in preventive strategies and integrated care to reduce chronic conditions later in life [41]. The study showed that young people in Brazil's northern, northeastern, and central western regions are less likely to experience multimorbidity than are those in the southern region. These regional disparities are possibly due to socioeconomic and health inequalities. For instance, the number of doctors per 1,000 inhabitants is three times greater in the southern region than in the northern region and double that in the northeastern and central western regions [44]. Additionally, young people in the southern region are three times more likely to have health insurance. These differences can lead to underdiagnosis in disadvantaged areas and overdiagnosis in wealthy regions, increasing the gap in the MLTC prevalence among different socioeconomic groups. This finding aligns with previous research suggesting that income can be positively associated with chronic diseases and multimorbidity in some low- and middle-income countries (LMICs) [14,23,24,28]. From a gender perspective, the study identified poor and fair self-perceived health, moderate and severe self-esteem issues and severe sleep disturbances as relevant risk factors for MLTC in both genders. However, the risk of multimorbidity associated with these factors was greater in young women, except for self-esteem issues, which were more common in men. Notably, factors such as education level, ethnicity, physical activity, urban residence, working conditions and smoking were not significant for either sex. The association between multimorbidity and self-rated health (SRH) was particularly strong among women with poor health, who had a 28.4% greater likelihood of having MLTC than did those in good health; for young men, this risk was 13.3%. Research indicates that SRH is a reliable indicator of future mental and physical health, and our findings confirm that poor SRH in adolescents increases the risk of MLTC in early adulthood, reflecting adaptation to long-term stress during this critical developmental period [45,46]. This study highlights that moderate to severe self-esteem is a significant risk factor for MLTC in both genders and particularly affects young men. Self-esteem, as noted by Huitt (2009), reflects an individual's sense of value and confidence [47]. Adolescence and young adulthood are crucial periods for psychological development and often coincide with a higher risk of mental disorders, especially among those with physical illnesses, leading to physical-mental multimorbidity [48,49]. Our research identified physical-mental multimorbidity combinations, such as asthma and depression, or triad combinations, such as asthma, depression, and other mental disorders. Decreased self-esteem may mediate these associations, which vary by sex. Adolescents with low self-esteem face long-term challenges, including being NEET (not employed, educated, or trained), financial difficulties, or criminal behavior. A Danish study revealed that young people aged 14 to 26 with physical-mental multimorbidity experience more psychosocial challenges and health risk behaviors than do those with only physical conditions [50]. Sleep is vital for well-being and mental health, especially for adolescents who need 8 to 10 hours each night [51]. Insufficient sleep can lead to behavioral changes, academic challenges, weight gain, and mood issues [51,52]. The study showed that young women are more likely to experience multimorbidity than young men are, regardless of sleep disturbance severity. The risk of multimorbidity also increases with the severity of sleep issues in both genders. These findings highlight the association between sleep disturbances and adverse physical and mental health outcomes, along with an increase in chronic conditions and cognitive impairments among those affected [52-54]. This study has several limitations. Self-reported chronic diseases may cause recall bias, particularly among illiterate individuals and those with lower education levels. To address this, only participants who could respond independently were included. The cross-sectional design limits our understanding of causality and may introduce bias from unmeasured confounding factors. Additionally, while the study examined multiple chronic conditions based on 14 diseases prevalent in Brazil, it excluded certain disorders, including autism spectrum disorders and eating disorders, which are common in adolescents and young adults. The varying age ranges analyzed also complicate consistent comparisons. Despite these limitations, the study has several strengths. The use of nationally representative data allows for the generalization of findings, and a larger sample size improves the accuracy of the results. The use of a parsimonious model yields reliable estimates, and the AME approach aids interpretation. Separate analyses by sex provide a more detailed understanding of the relationships between multimorbidity and potential predictors in Brazilian youth. Future research in Brazil should focus on (1) developing a framework to understand MLTC in childhood and adolescence, including definitions and patterns of physical and mental chronic conditions, their evolution, and severity; (2) longitudinal studies with clinical data to accurately track diagnoses, disease severity, and progression over time; (3) investigating the impact of socioeconomic inequalities on the risk of MLTC in adolescents and young adults; and (4) examining how specific combinations of chronic diseases determining MLTC impact healthcare expenditures to evaluate the effectiveness of National Health System (SUS) policies in reducing inequalities. In conclusion, this study revealed a significant association between chronic physical illnesses and mental health disorders in young people with MLTC, with varying influences based on sex. For women, particular risk factors include excessive computer use at home, having health insurance, and belonging to higher income brackets. For young men, moderate consumption of ultra-processed foods is particularly concerning. Additionally, regional protective factors are stronger for women, while working conditions tend to favor men. The findings highlight the importance of psychosocial and socioeconomic factors in determining the risk of MLTC, emphasizing the need for public policies that promote health and address social inequalities among young people. Declarations Acknowledgments : The authors utilized AI-Grammarly to enhance the manuscript’s grammar and language understanding. Conflicts of interest : The authors declare no conflicts of interest. Funding : This work is not supported by any external funding. Acknowledgments : The authors utilized AI-Grammarly to enhance the manuscript's grammar and language clarity. Ethics approval: Our study was exempted from institutional ethics committee approval because it used publicly available secondary data. The 2019 National Health Survey was submitted to the National Research Ethics Committee/National Health Council and approved by Opinion No. 3,529,376, issued on August 23, 2019. Availability of data and material : Open data from the public domain were used. Authors' contributions : Pedro Olivares-Tirado contributed to the writing-original draft, conceptualization, formal analysis, investigation, methodology, project administration and edition. Rosendo Zanga contributed to the formal analysis, investigation, methodology and editing. Julieta Aránguiz-Ramírez contributed to the investigation, methodology, project administration and editing. References The King's Fund. Long-term conditions and multimorbidity. Part of Time to Think Differently. 2019. Available from https://www.kingsfund.org.uk/projects/time-think-differently/trends-disease-and-disability-long-term-conditions-multimorbidity Skou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ et al. Multimorbidity. 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However, the term has been redefined as \"multiple long-term conditions\"(MLTC) by the National Institute for Health and Care Research (NIHR, UK) to address concerns about negative connotations for patients and narrow biomedical care approaches [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. MLTC was defined as having two or more long-term conditions, including physical and mental health conditions or single conditions with multiple system impacts [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThere is clear evidence of a link between MLTCs and age, sex and socioeconomic status [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Multiple long-term conditions are strongly associated with age, with a higher prevalence in older people but a rapid increase in middle-aged people [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The longevity population, which in part reflects improvements in survival from acute and chronic conditions; however, other risk factors, such as income inequality and poverty; and changes in lifestyles, i.e., physical inactivity and sedentary or unhealthy diets, seem to be consistently associated with an increased risk of MLTCs at earlier ages [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe prevalence of multiple long-term conditions is increasing, and these conditions are more prevalent in older people, women and socioeconomically disadvantaged groups across the wealthiest countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Recent studies have shown that the overall global prevalence of multimorbidity in the adult population fluctuates between 30 and 50%, depending on methodological factors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A recent systematic review and meta-analysis revealed that the overall global prevalence of MLTC was 37.2%, and that of South America was 45.7%, the highest prevalence of multimorbidity worldwide [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIndividuals with multiple morbidities face a higher risk of hospital admission disability, worse quality of life and premature death. They also often grapple with functional and/or mental health issues, polypharmacy, and increasing economic costs [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, the MLTC significantly impacts the healthcare system by increasing the utilization of healthcare services [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and has a significant impact on work productivity, absenteeism and presenteeism rates, particularly in young workers [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBased on National Health Survey data (2019), studies in Brazil showed that the overall prevalence of MLTC in adults was 29.5%, with 35.9% in women and 22.1% in men. The MLTC rate increases with age from 18.7\u0026ndash;29.9% from 2013 to 2019 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, the overall prevalence of MLTC in the young adult population (18\u0026ndash;29) in Brazil was approximately 9.0% [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eResearch on multiple long-term conditions is recognized as an urgent global priority. However, few studies have focused on multiple morbidities in adolescents and young adults, creating a gap in the scientific evidence [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Individuals in this age group, often perceived as relatively healthy, are experiencing increasing incidences of chronic diseases such as obesity, diabetes, asthma, depression, and anxiety [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], particularly in low- and middle-income countries (LMICs), where chronic diseases reach the levels observed in high-income countries (HICs) [\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdolescence and early adulthood are critical periods during which young people adopt various lifestyle behaviors that significantly impact their long-term health outcomes and overall well-being. Unhealthy lifestyle behaviors, including poor diet, physical inactivity, smoking, substance use, and environmental stressors, are major contributors to multimorbidity risk in youth. These behaviors often cluster together and are influenced by peer norms, media exposure, and sociocultural environments [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, research has consistently shown that people in the lowest income brackets who live in the most deprived areas in HICs have a greater risk of multimorbidity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Furthermore, studies across LMICs have shown inconsistent associations between income and the MLTC, reporting an increasing risk of MLTC with increasing income [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUnderstanding the factors that contribute to multimorbidity among young people is essential, particularly for vulnerable groups in LMICs. This understanding is vital for achieving Sustainable Development Goal 3.4 (SDG 3.4), which aims to reduce premature deaths from no communicable diseases (NCDs) by one-third through prevention and treatment by 2030 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study aimed to investigate the associations between individual demographic, socioeconomic, health, and lifestyle factors and the risk of multiple long-term conditions in Brazilian adolescents and early adults. This study will focus on gender differences and provide empirical evidence to inform public health policies aimed at promoting early intervention and preventing chronic diseases among at-risk young individuals.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cstrong\u003eData and sample population\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis observational cross-sectional study is based on the Brazilian National Health Survey 2019 (NHS-2019), which was conducted by the Brazilian Institute of Geography and Statistics (IBGE) in collaboration with the Ministry of Health. The survey represents Brazil\u0026apos;s noninstitutionalized population and covers national, regional, state, and metropolitan levels. The sample was drawn from an IBGE master sample, which was stratified into three cluster stages: census tracts chosen with proportional probability, randomly selected households, and individuals aged 15 or older chosen from each household. Interviews were conducted between August 2019 and March 2020 using smartphones programmed with the survey questionnaire. A total of 90,846 households and 275,323 individuals participated, for a total response rate of 93.6% [30]. This study focused on participants aged 15 to 29 who were capable of responding independently and provided complete information for the relevant variables, resulting in a sample of 17,708 individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDependent variable\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultimorbidity or MLTC was assessed by the number of self-reported physician diagnoses of 14 chronic diseases. These conditions include hypertension, diabetes mellitus, hypercholesterolemia, heart problems, stroke, asthma or wheezing, arthritis, work-related musculoskeletal disorders (WMSD), depression, other mental health disorders, chronic obstructive pulmonary disease (COPD), cancer, chronic kidney failure and other chronic diseases. Participants reported their diagnoses in response to the question \u0026ldquo;Has any doctor ever diagnosed you with...? For depression, the question was \u0026ldquo;Has any doctor or mental health professional ever diagnosed you with depression?\u0026rdquo; Responses were yes or no. MLTC was defined as having at least two of the fourteen selected conditions mentioned above and was categorized as a dichotomous variable (1 = multiple conditions; 0 = none or one chronic disease).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndividual factors\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe final models included factors such as age,\u0026nbsp;sex, ethnicity, self-esteem, and lack of interest or anhedonia. Participants were divided into age groups: 15-19 years (reference), 20-24 years, and 25-29 years. Gender was a binary variable, with men\u0026nbsp;serving\u0026nbsp;as the reference.\u0026nbsp;The ethnicity\u0026nbsp;categories included white, black, brown-skinned, yellow, and indigenous, with yellow and indigenous combined as the reference due to sample size. Self-esteem was measured with the question \u0026quot;In the last two weeks, how often have you felt bad about yourself or let your family down?\u0026quot; Lack of interest and anhedonia\u0026nbsp;were\u0026nbsp;assessed with\u0026nbsp;the following item:\u0026nbsp;\u0026quot;In the last two weeks, how often did you have little interest or no pleasure in doing things?\u0026quot;\u0026nbsp;The responses\u0026nbsp;ranged from \u0026quot;none of the days\u0026quot; to \u0026quot;almost every day.\u0026quot;\u0026nbsp;The responses\u0026nbsp;were grouped into three categories: \u0026quot;no days\u0026quot;,\u0026nbsp;\u0026quot;moderate\u0026quot; (less than half the days), and \u0026quot;severe\u0026quot; (more than half the days), with \u0026quot;no days\u0026quot;\u0026nbsp;serving\u0026nbsp;as the reference group.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeographic and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003esociodemographic\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;factors\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final models included region, urban residence, education level, income deciles, work situation, and current occupation. Brazil is divided into five regions\u0026mdash;North, Northeast, Central-West, Southeast, and South\u0026mdash;using the South as the reference\u0026nbsp;region\u0026nbsp;due to its\u0026nbsp;greater socioeconomic\u0026nbsp;development. Residence\u0026nbsp;was\u0026nbsp;classified as urban or rural, with rural as the reference. Education is divided into three categories: illiterate/elementary, high school, and\u0026nbsp;graduate, with\u0026nbsp;graduate education serving\u0026nbsp;as the reference. Household income deciles reflect the gross monthly income of employed members, excluding pensioners and domestic employees.\u0026nbsp;The work\u0026nbsp;situation\u0026nbsp;was\u0026nbsp;binary, with individuals not working during the reference week\u0026nbsp;serving\u0026nbsp;as the reference group. Current occupation is grouped into five categories: no occupation, elementary\u0026nbsp;occupation, skilled\u0026nbsp;worker, service\u0026nbsp;worker, and\u0026nbsp;professional/manager, with professionals/managers\u0026nbsp;serving\u0026nbsp;as the reference group.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealth and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003elifestyle\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;risk factors\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHealth insurance was categorized as a dichotomous variable, with those lacking coverage serving as the reference group. Self-rated health was divided into three groups: good/very good, fair, and bad/very bad, with good/very good serving as the reference. According to WHO guidelines, adults should engage in 150 to 300 minutes of moderate-intensity or 75 to 150 minutes of vigorous-intensity aerobic activity each week [31]. Physical activity was assessed through questions about days spent on sports or recreational activities and the daily time spent on these activities, resulting in three categories: physically inactive (0 to 150 min/week), recommended (150 to 300 min/week), and over trained (more than 300 min/week), with the recommended category serving as the reference. Sedentary behavior was measured by the time spent watching television and using electronic devices at home, categorized as little time (less than 2 hr./day), moderate time (2 to 6 hr./day), or much time (6 hr. or more), with the little time group serving as the reference.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSleep disturbances over the past two weeks were categorized as no days, moderate (approximately half the days), or severe (almost daily), with \u0026quot;no days\u0026quot; as the reference group. The survey questionnaire assessed the previous day\u0026apos;s consumption of 12 natural and 10 ultra-processed foods. An unhealthy diet was defined by the proportion of ultra-processed foods classified as mild (20% or less), moderate (20%\u0026ndash;50%), or severe (over 50%), with mild status serving as the reference.\u0026nbsp;Tobacco and alcohol use were included as dichotomous variables (1 = yes; 0 = no).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were used to analyze the geographic, sociodemographic, health, and lifestyle characteristics of young individuals in Brazil, focusing on sex and multimorbidity. The sample design was incorporated into the analysis, enabling valid inferences for the population. The analysis employed the \u003cem\u003esvyset\u003c/em\u003e and \u003cem\u003esvy\u003c/em\u003e commands to incorporate the primary sampling unit, individual weights, and strata. Multicollinearity was assessed using correlation matrices and variance inflation factors (VIFs). Finally, multivariate logistic regression models were constructed to examine multiple long-term conditions by sex using both raw and survey data sets.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel specification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate\u0026nbsp;analysis was initially performed for each variable, focusing on those with p values less than 0.25 based on the Wald test from logistic regression. A purposeful approach was then used to refine covariate selection [32]. Variables with significance levels of 0.1 or 0.15 were included, while no significant\u0026nbsp;covariates not considered confounders were removed. Odds ratios (ORs), standard errors, and 95% confidence intervals were estimated. Model goodness-of-fit was assessed using the\u0026nbsp;Hosmer\u0026ndash;Lemeshow\u0026nbsp;test, and predictive ability was evaluated with the area under the curve (AUC). Average marginal effects (AMEs) were calculated using the \u003cem\u003edx/dy\u003c/em\u003e command and the delta method for standard errors to show how predicted probabilities for multimorbidity change as predictors shift from the reference to the interest category while controlling for the other variables [33]. Statistical significance was determined using Wald\u0026apos;s chi-square test, accepting a 10% significance level due to the exploratory nature of the study, with analyses conducted using STATA version 14.0 (StataCorp, TX, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 2019, 17,708 young people aged 15 to 29 years were interviewed, with an average age of 22.9 years (SD=4.15). In the survey sample, 8.02% (95% CI: 7.72; 8.32) reported multiple long-term conditions (MLTC), which was slightly greater than the 7.23% reported in the raw data. Most individuals with MLTC were women (61.0%), with an average age of 23.6 years (SD: 4.01) and a prevalence of 9.7% (95% CI: 9.2; 10.2). In men, the prevalence was 6.3% (95% CI: 5.9; 6.7). Prevalence varied by age: 6.3% (95% CI: 5.9; 6.8) for 15-19-year-olds, 9.0% (95% CI: 8.5; 9.5) for 20-24-year-olds, and 8.8% (95% CI: 8.2; 9.4) for those aged 25-29 years. Table 1 details the sample characteristics related to sex and MLTC among Brazilian youth.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eSample characteristics according to sex and multimorbidity status in young Brazilian people. NHS-2019\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003enon-MLTC (n:8,379)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMLTC (n:872)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003enon-MLTC (n:8,048)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMLTC (n:409)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge groups (n,%)\u0026nbsp;\u003c/strong\u003e ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e15-19-year-olds (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,059 (24.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e154 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,040 (25.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e82 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e20-24-year-olds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,930 (35.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e316 (36.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,751 (34.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e127 (31.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e25-29-year-olds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3,390 (40.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e402 (46.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3,257 (40.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e200 (48.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity (n,%)***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003ewhite (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,492 (29.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e318 (36.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,458 (30.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e169 (41.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eblack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e882 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e115 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e978 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e47 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003ebrown-skinned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4,880 (58.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e423 (48.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4,493 (55.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e187 (45.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eothers(yellow, indigenous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e125 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e16 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e119 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e6 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation levels (n,%) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u0026nbsp;Illiterate/elementary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,161 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e196 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,565 (31.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e106 (25.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003ehigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4,376 (52.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e432 (49.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4,084 (50.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e189 (46.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003egraduated (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,842 (22.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e244 (28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,399 (17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e114 (27.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban residence (n, %) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6,482 (77.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e725 (83.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5,959 (74.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e346 (84.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion (n,%) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003enorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,102 (25.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e160 (18.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,046 (25.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e71 (17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003enortheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3,067 (36.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e257 (29.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,854 (35.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e114 (27.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003ecentral west\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e997 (11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e103 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e921 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e46 (11.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003esoutheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,421 (17.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e216 (24.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,428 (17.7%0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e109 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003esouth (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e792 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e136 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e799 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e69 (16.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking (n,%) **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3,428 (40.9%)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e423 (48.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5,069 (63.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e235 (57.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking Occupation ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u0026nbsp;no occupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4,539 (54.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e405 (46.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,484 (30.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e156 (38.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eelementary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e596 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e68 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e921 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e75 (18.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003equalified, skilled and artisans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e348 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e41 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,386 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e77 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eadministrative support and services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,093 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e233 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,968 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e64 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eProfessionals and technicians (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e803 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e125 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,289 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e37 (9.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold income deciles (n,%) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e1st decile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,310 (15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e107 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e963 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e27 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e2nd decile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,185 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e92 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e995 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e45 (11.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e3rd decile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e965 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e98 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e842 (10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e39 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e4th decile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e846 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e76 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e831 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e40 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e5th decile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e767 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e78 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e833 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e46 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e6th decile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e741 (9.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e72 (8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e730 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e28 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e7th decile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e732 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e93 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e782 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e40 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e8th decile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e648 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e84 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e741 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e43 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e9th decile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e569 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e75 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e645 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e47 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e10th decile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e473 (5.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e82 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e556 (7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e53 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth insurance(n,%) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,351 (16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e233 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,349 (16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e117 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-rated health status ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003egood/very good (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6,763 (80.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e518 (59.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6,909 (85.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e281 (68.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003efair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,508 (18.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e295 (33.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,076 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e101 (24.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003ebad/very bad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e108 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e59 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e63 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e27 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLack of interest or\u003c/strong\u003e a\u003cstrong\u003enhedonia ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eno day (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5,481 (65.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e332(38.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6,236 (77.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e226 (55.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003emoderate (less than half of days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1991 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e268 (30.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,398 (17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e109 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003esevere (almost every day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e907 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e272 (31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e414 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e74 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-esteem problems ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eno day (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6,628 (79.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e423 (48.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e7,084 (88.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e262 (64.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003emoderate (less than half of days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1117 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e196 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e694 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e87 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003esevere (almost every day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e634 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e253 (29.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e270 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e60 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity the last week (n,%) **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003einactive(\u0026lt; 150 min/week)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6,121 (73.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e588 (67.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4,768 (59.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e258 (63.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003erecommended(150\u0026ndash;300 min/week) (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,380 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e164 (18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,624 (20.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e66 (16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eOver-trained (\u0026gt;300 min/week)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e878 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e120 (13.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,656 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e85 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSitting time watching television (n,%) **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003elittle time (do not - \u0026nbsp; \u0026nbsp; \u0026lt; 2 hours/day) (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4,876 (58.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e545 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5,079 (63.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e277 (67.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003emoderate time (2 hours - \u0026lt; 6 hours/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,954 (35.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e274 (31.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,633 (32.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e109 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003emany time (\u003cu\u003e\u0026gt;\u003c/u\u003e 6 hours/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e549 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e53 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e336 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e23 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSitting time using computer (n,%) **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003elittle time (do not - \u0026nbsp; \u0026nbsp; \u0026lt;2 hours/day) (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3,082 (36.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e259 (29.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3,118 (38.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e131 (32.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003emoderate time (1 hour - \u0026lt; 6 hours/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3,499 (41.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e380 (43.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3,355 (41.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e184 (45.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003emany time (\u003cu\u003e\u0026gt;\u003c/u\u003e 6 hours/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,798 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e233 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,575 (19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e94 (23.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep disturbances \u0026nbsp; \u0026nbsp; (n,%)***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eno day (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5,820 (69.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e323 (37.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6,380 (79.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e200 (48.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003emoderate(around half of the days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,479 (17.7%0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e225 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,032 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e99 (24.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003esevere (almost every day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,080 (12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e324 (37.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e636 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e110 (26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConsumpt. of ultra-processed foods **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003emild (\u0026lt; = 20% of total \u0026nbsp; \u0026nbsp; daily food) (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,269 (27.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e187 (21.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,981 (24.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e88 (21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003emoderate (\u0026gt; 20% to \u0026nbsp; \u0026nbsp; \u0026lt; 50% )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4,568 (54.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e510 (59.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4,667 (58.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e254 (62.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003ehigh (\u0026gt; = 50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,494 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e168 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,370 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e64 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoker **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e446 (5.3%)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e88 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,178 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e83 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol consumption ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,945 (35.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e369 (42.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4,223 (52.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e220 (53.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 535px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMLTC\u003c/strong\u003e: Multiple Long-term Conditions or Multimorbidity. (\u003cstrong\u003eref\u003c/strong\u003e): reference group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 535px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eⁿᶳ\u003c/strong\u003e: no significant; \u003cstrong\u003e*\u003c/strong\u003e: p \u0026lt; 0.1; \u003cstrong\u003e**\u003c/strong\u003e: p \u0026lt; 0.05;\u003cstrong\u003e\u0026nbsp;***\u003c/strong\u003e: p \u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe main prevalence rates of chronic diseases in the sample were as follows: asthma (6.3%), other mental disorders (5.6%), depression (5.2%), other chronic diseases (4.7%), hypertension (4.2%), and hypercholesterolemia (3.7%). Both depression and other mental disorders accounted for 44% of the multimorbidity cases, while asthma contributed 32.2%. In terms of multimorbidity status among young men, 79.7% reported having two conditions, 15.2% had three, and 5.1% had four or more. The most common combinations for those with two conditions included depression and other mental disorders (19.9%), asthma with other chronic diseases (6.7%) and asthma and other mental disorders (5.5%). For those with three or more conditions, notable combinations included depression, other mental disorders, and other chronic diseases (14.5%); asthma, depression, and other mental disorders (11.3%); and asthma, hypertension and hypercholesterolemia (4.8%).\u003c/p\u003e\n\u003cp\u003eAmong young women, 70.8% had two chronic conditions, 22.0% had three, and 7.2% had four or more. Common combinations for those with two conditions included depression and other mental disorders (21.2%), hypertension and hypercholesterolemia (5.2%), and asthma with depression (4.5%). For women with three or more conditions, notable combinations included asthma, depression and other mental disorders (9.9%), depression, hypercholesterolemia and other mental disorders (7.3%), and depression, other mental disorders and other chronic diseases (7.3%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Goodness of Fit Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe multicollinearity diagnostic revealed that most variables exhibited low correlations (r \u0026lt; .30), except for the following pairs: work situation and current occupation, self-esteem and anhedonia, sleep disturbances and anhedonia, deciles and health insurance, and deciles and education levels, which showed moderate correlations. The mean variance inflation factor (VIF) was low at 1.36, indicating no multicollinearity in the models. Goodness-of-fit tests confirmed the model\u0026apos;s significance, and the area under the ROC curve (AUC) showed moderate predictive power.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiple\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003elong\u003c/strong\u003e\u003cstrong\u003e-term\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econditions\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;in the overall sample.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of the raw data model identified several key predictors of multimorbidity among young people in Brazil. These included being female, aged \u0026gt;20-29 years, from higher income groups, rating health as fair or poor, having health insurance, experiencing anhedonia and low self-esteem, prolonged computer use, sleep issues, consuming ultra-processed foods, and smoking. Protective factors included living in specific regions, watching moderate television, holding elementary or skilled jobs, and consuming alcohol. Notably, education level and physical activity were not significantly related.\u003c/p\u003e\n\u003cp\u003eThe survey data model identified fewer significant predictors of multimorbidity than did the raw data model, with some moderate changes in magnitude and significance. Key predictors included age \u0026gt;20-29 years, a rating of health as fair or poor, severe lack of interest or anhedonia, and moderate to severe self-esteem. Additional factors included moderate time spent on computer use at home, sleep disturbances, having health insurance, higher socioeconomic status, and moderate consumption of ultra-processed foods, although their p values were approximately 0.10. The protective factors identified include living in the northern, northeastern, or central western regions; holding skilled jobs; and consuming moderate amounts of alcohol. However, gender, ethnicity, education level, physical activity, and television viewing time were not significantly related to the participants.\u003c/p\u003e\n\u003cp\u003eTable 2 shows the odds ratios (ORs) and average marginal effects (AMEs) with 95% confidence intervals (CIs) from the survey data logistic regression model examining multimorbidity in the overall sample of young Brazilian people.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"635\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" rowspan=\"2\" style=\"width: 625px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003ePredictors and Marginal Effects of Multimorbidity from survey data of the overall sample\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"30\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLogistic Regression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 256px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Marginal Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[95% C.I.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edy/dx\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[95% C.I.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e20-24-year-olds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.661 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(1.196 - 2.308)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(0.011 - 0.051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e25-29-year-olds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.560 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(1.140 - 2.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(0.008 - 0.045)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.048 \u0026nbsp;ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.796 - 1.380)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.014 - 0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eblack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.082 \u0026nbsp;ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.750 - 1.561)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.020 - 0.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003ebrown-skinned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.917 \u0026nbsp;ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.710 - 1.185)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.005 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.022 - 0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eothers(yellow, indigenous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.787 \u0026nbsp;ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.364 - 1.705)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.014 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.058 - 0.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"35\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eIlliterate/elementary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.922 \u0026nbsp;ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.630 - 1.349)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.006 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.031 - 0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"31\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003ehigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.805 \u0026nbsp;ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.582 - 1.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.014 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.036 - 0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.943 \u0026nbsp;ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.711 - 1.250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.004 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.022 - 0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003enorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.458 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.321 - 0.652)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.051 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.074 - -0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003enortheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.463 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.334 - 0.643)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.050 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.073 - -0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003ecentral west\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.560 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.389 - 0.806)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.040 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.065 - -0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003esoutheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.797 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.598 - 1.063)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.018 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.040 - 0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.064 \u0026nbsp;ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.679 - 1.667)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.025 - 0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking occupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;no occupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.930 \u0026nbsp;ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.529 - 1.634)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.005 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.046 - 0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eelementary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.646 \u0026nbsp;ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.370 - 1.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.028 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.063 - 0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003equalified, skilled and artisans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.520 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.321 - 0.841)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.039 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.068 - -0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"39\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eadministrative and services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.737 \u0026nbsp;ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.503 - 1.080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.020 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.047 - 0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"38\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold income deciles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.062 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(1.006 - 1.121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(0.000 - 0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"34\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ehealth insurance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.278 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.964 - 1.694)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.003 - 0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-rated health status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"32\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003efair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.736 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(2.124 - 3.524)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.081 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(0.056 - 0.106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003ebad/very bad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.805 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(4.601 - 1.324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.230 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(0.139 - 0.320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLack of interest/anhedonia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"30\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.161 \u0026nbsp;ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.860 - 1.568)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.010 - 0.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003esevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.402 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(1.012 - 1.942)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(0.000 - 0.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-esteem problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.799 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(1.236 - 2.616)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.040 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(0.011 - 0.069)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003esevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.350 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.5782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(2.388 - 4.699)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.104 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(0.066 - 0.141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003einactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.909 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.679 - 1.218)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.006 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.025 - 0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eover-trained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.983 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.665 - 1.453)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.001 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.027 - 0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSitting time watching TV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"30\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003emoderate time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.915 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.714 - 1.171)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.006 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.021 - 0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003emany time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.442 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.874 - 2.378)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.014 - 0.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSitting time using computer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"31\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003emoderate time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.427 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(1.093 - 1.864)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(0.006 - 0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003emany time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.142 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.843 - 1.547)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.010 - 0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep disturbances\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.755 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(1.291 - 2.387)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(0.014 - 0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003esevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.282 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.5238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(2.401 - 4.488)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.095 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(0.064 - 0.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUltra-processed foods intake\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"35\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.268 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.978 - 1.643)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.001 - 0.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.215 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.855 - 1.727)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.010 - 0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.989 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.663 - 1.475)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.001 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.026 - 0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.760 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.602 - 0.961)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.017 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e(-0.032 - -0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003econstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.012 - 0.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 625px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edy/dx\u003c/strong\u003e: the marginal change of the outcome variable(dy) concerning the change from the reference group of the factors(dx).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 369px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eⁿᶳ\u003c/strong\u003e : no significant; \u003cstrong\u003e*\u003c/strong\u003e: p \u0026lt; 0.1; \u003cstrong\u003e**\u003c/strong\u003e: p \u0026lt; 0.05; \u003cstrong\u003e***\u003c/strong\u003e: p \u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eYoung individuals in poor or fair health are significantly more likely to experience multimorbidity, with rates of 23.0% and 8.1%, respectively, than are those in good or very good health. Those with severe self-esteem issues were 10.4% more likely to have multiple morbidities, while those with moderate self-esteem issues were 4.0% more likely. Severe sleep disturbances increase the likelihood of multimorbidity by 9.5%. Individuals aged \u0026gt;20-24 years and \u0026gt;20-29 years with health insurance and higher incomes had a less than 3.0% increased likelihood of multimorbidity, while those in the northern and central western regions were less likely to experience it than their peers in the southern region were. Additionally, individuals in skilled or artisan jobs are 3.9% less likely to suffer from multimorbidity than professional workers are, and alcohol consumers are 1.8% less likely to be affected than no consumers are.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiple\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003elong\u003c/strong\u003e\u003cstrong\u003e-term\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econditions\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;in young women\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of women\u0026apos;s raw data revealed key predictors of multimorbidity, including age \u0026gt;20-29 years, having health insurance, having a higher income, having poor self-rated health, lacking interest or anhedonia, self-esteem issues, high computer use, sleep disturbances, moderate consumption of ultra-processed foods, and smoking. Protective factors included being brown-skinned, living in northern or central western regions, physical inactivity compared to those who met activity recommendations, and alcohol consumption. Education levels, television viewing time, and occupational activities were not significantly different.\u003c/p\u003e\n\u003cp\u003eTable 3 presents the odds ratios (ORs) and average marginal effects (AMEs) with 95% confidence intervals (95% CIs) from the survey data logistic regression model on multimorbidity among young Brazilian women.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 605px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003ePredictors and Marginal Effects of Multimorbidity\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003efrom survey data in young women.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLogistic Regression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Marginal Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[95% C.I.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edy/dx\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[95% C.I.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e20-24-year-olds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.280 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.882 - 1.857)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.008 - 0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e25-29-year-olds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.516 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(1.039 - 2.211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.004 - 0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eblack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1009 ⁿᶳ.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.612 - 1.665)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.039 - 0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003ebrown-skinned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.810 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.602 - 1.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.016 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.038 - 0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eothers(yellow, indigenous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.887 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.360 - 2.190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.009 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.075 - 0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eIlliterate/elementary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.024 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.653 - 1.605)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.034 - 0.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003ehigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.819 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.574 - 1.170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.015 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.042 - 0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.795 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.568 - 1.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e;-0.018 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.022 - 0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003enorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.585 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.383 - 0.895)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.046 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.083 - -0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003enortheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.475 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.317 - 0.711)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.061 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.095 - -0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003ecentral west\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.484 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.293 - 0.800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.059 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.099 - -0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003esoutheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.596 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.412 - 0.862)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.045 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.079 -- 0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.305 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.3811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.737 - 2.314)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.023 - 0.063)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking occupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;no occupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.142 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.568 - 2.295)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.042 - 0.063)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eelementary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.831 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.438 - 1.575)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.013 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.056 - 0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003equalified, skilled and artisans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.262 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.659 - 2.419)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.034 - 0.070)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eadministrative and services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.912 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.563 - 1.476)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.007 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.041 - 0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold income deciles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.082 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(1.015 - 1.152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.001 - 0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ehealth insurance(n,%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.610 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(1.165 - 2.224)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.010 - 0.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-rated health status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003efair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.899 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(2.137 - 3.932)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.097 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.064 - 0.130)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003ebad/very bad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.546 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e31.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(5.045 - 18.064)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.284 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.169 - 0.400)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLack of interest/anhedonia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.162 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.834 - 1.618)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.013 - 0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003esevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.369 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.929 - 2.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.007 - 0.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-esteem problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.606 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(1.134 - 2.276)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.007 - 0.064)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003esevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.005 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.5471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(2.103 - 4.294)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.102 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.062 - 0.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003einactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.838 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.606 - 1.159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.014 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.039 - 0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eover-trained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.819 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.513 - 1.309)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;-0.015 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.050 - 0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSitting time watching TV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003emoderate time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.094 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.820 - 1.460)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.015 - 0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003emany time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.245 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.614 - 2.522)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.041 - 0.075)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSitting time using computer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003emoderate time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.487 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(1.056 - 2.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.004 - 0.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003emany time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.451 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(1.007 - 2.090)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.00005 - 0.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep disturbances\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.125 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(1.428 - 3.164)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.054 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.021 - 0.087)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003esevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.924 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.6979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(2.769 - 5.562)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.123 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.083 - 0.162)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUltra-processed foods intake\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.137 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.838 - 1.544)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.012 - 0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.179 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.772 - 1.799)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.019 - 0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1036 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.638 - 1.683)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.034 - 0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.875 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.670 - 1.141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.010 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.029 - 0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003econstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e(0.009 - 0.070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 605px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edy/dx\u003c/strong\u003e: the marginal change of the outcome variable(dy) concerning the change from the reference group of the factors(dx).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 378px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eⁿᶳ\u003c/strong\u003e : no significant; \u003cstrong\u003e*\u003c/strong\u003e: p \u0026lt; 0.1 ; \u003cstrong\u003e**\u003c/strong\u003e: p \u0026lt; 0.05 ; \u003cstrong\u003e***\u003c/strong\u003e: p \u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eKey predictors of multimorbidity include age 25-29 years, a rating of health as fair or poor, moderate to severe self-esteem issues, and sleep disturbance or spending significant time on computers. Having health insurance and a higher socioeconomic status are also important factors. In contrast, living in the northern, northeastern, central western, or southeastern regions served as protective factors compared to living in the southern region.\u003c/p\u003e\n\u003cp\u003eAccording to the AMEs from the women\u0026apos;s survey data, individuals in poor or fair health are more likely to experience multiple morbidities, with rates of 28.4% and 9.7%, respectively, than are those in good health. Severe sleep disturbances increase the likelihood of multimorbidity by 12.3%, while moderate disturbances increase this likelihood by 5.3%. Similarly, those with severe self-esteem problems are 10.2% more likely to face multimorbidity, and those with moderate self-esteem problems are 3.6% more likely. Compared with their reference group, women aged 25-29 years with health insurance, higher income, and significant computer use had a 3.0% increased risk. Conversely, young women from the northern, northeastern, central western, and southeastern regions are less likely (4.5% - 6.0%) to experience multimorbidity than are those from the southern region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiple\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003elong\u003c/strong\u003e\u003cstrong\u003e-term\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econditions\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;in young men\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of the raw data from the men revealed several key predictors of multimorbidity, including age 25-29 years, having health insurance, rating health as fair or poor, experiencing anhedonia, having self-esteem issues, physical inactivity, and sleep disturbances. Protective factors included living in the northern, northeastern, or central western regions; holding elementary or skilled jobs; and spending moderate amounts of time watching television. Education levels, time spent using computers at home, ultra-processed food consumption, smoking status, and alcohol use were not significantly different.\u003c/p\u003e\n\u003cp\u003eTable 4 presents the odds ratios (ORs) and average marginal effects (AMEs) with 95% confidence intervals (95% CIs) from the survey data logistic regression model of multimorbidity among young Brazilian men.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"593\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 593px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003ePredictors and Marginal Effects of Multimorbidity\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003efrom survey data in young men.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLogistic Regression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Marginal Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[95% C.I.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edy/dx\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[95% C.I.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e20-24-year-olds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.415 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.6837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(1.386 - 4.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.044 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.015 - 0.073)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e25-29-year-olds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.769 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(1.042 - 3.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.002 - 0.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eblack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.184 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.3243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.692 - 2.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.021 - 0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003ebrown-skinned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.027 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.683 - 1.546)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.020 - 0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eothers(yellow, indigenous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.520 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.3959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.117 - 2.313)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.026 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.073 - 0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eIlliterate/elementary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.848 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.463 - 1.550)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.009 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.042 - 0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003ehigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.811 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.480 - 1.369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;-0.011 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.040 - 0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.157 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.3001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.696 - 1.924)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.017 - 0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003enorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.303 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.161 - 0.569)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.050 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.079 - -0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003enortheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.430 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.243 - 0.760)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.040 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.069 - -0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003ecentral west\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.749 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.380 - 1.474)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.017 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.054 - 0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003esoutheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.090 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.673 - 1.764)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.025 - 0.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.916 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.3039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.478 - 1.756)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.005 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.038 - 0.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking occupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;no occupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.912 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.3709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.411 - 2.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.006 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.059 - 0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eelementary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.499 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.219 - 1.134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.038 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.082 - 0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003equalified, skilled and artisans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.339 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.178 - 0.648)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.052 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.085 - -0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eadministrative and services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.557 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.307 - 1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.033 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.068 - 0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold income deciles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.035 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.949 - 1.128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.003 - 0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ehealth insurance(n,%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.953 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.592 - 1.535)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.002 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.027 - 0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-rated health status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003efair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.499 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.5496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(1.623 - 3.846)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.060 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.025 - 0.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003ebad/very bad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.907 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e26.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(1.732 - 13.3906)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.133 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.001 - 0.264)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLack of interest/anhedonia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.147 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.714 - 1.841)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.018 - 0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003esevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.466 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.825 - 2.604)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.014 - 0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-esteem problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.451 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.8572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(1.234 - 4.865)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.057 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.001 - 0.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003esevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.186 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e12.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(2.337 - 7.498)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.111 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.047 - 0.175)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003einactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.017 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.625 - 1.655)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.024 - 0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eover-trained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.104 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.3241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.621 - 1.963)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.025 - 0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSitting time watching TV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003emoderate time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.688 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.445 - 1.065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.018 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.038 - 0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003emany time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.603 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.5378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.831 - 3.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.030 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.017 - 0.078)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSitting time using computer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003emoderate time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.313 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.873 - 1.975)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.007 - 0.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003emany time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.759 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.442 - 1.302)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.012 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.036 - 0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep disturbances\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.333 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.876 - 2.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.007 - 0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003esevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.787 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.7605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(1.632 - 4.758)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.069 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.023 - 0.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUltra-processed foods intake\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.563 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.3638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.991 - 2.467)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.001 - 0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.373 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.759 - 2.485)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.013 - 0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.043 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.3259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.565 - 1.924)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002 ⁿᶳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.030 - 0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.624 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.430 - 0.907)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.025 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.044 - -0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003econstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.007 - 0.110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 593px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edy/dx\u003c/strong\u003e: the marginal change of the outcome variable(dy) concerning the change from the reference group of the factors(dx).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eⁿᶳ\u003c/strong\u003e : no significant; \u003cstrong\u003e*\u003c/strong\u003e: p \u0026lt; 0.1 ; \u003cstrong\u003e**\u003c/strong\u003e: p \u0026lt; 0.05 ; \u003cstrong\u003e***\u003c/strong\u003e: p \u0026lt; 0.0001\u003c/p\u003e\n\u003cp\u003eKey predictors from the survey data model include age \u0026gt;20 to 29 years, rating one\u0026rsquo;s health as fair or poor, having moderate to severe self-esteem issues, and experiencing severe sleep disturbances. Moderate consumption of ultra-processed foods also plays a role. Protective factors include living in the northern, northeastern, central western, or southeastern regions compared to the southern region and working in elementary, skilled, or administrative jobs instead of professional roles. Moderate television viewing and alcohol consumption are also protective. Factors such as ethnicity, education level, physical activity, lack of interest or anhedonia, computer use at home, and smoking were not significantly different.\u003c/p\u003e\n\u003cp\u003eAccording to the AMEs, individuals in poor or fair health have 13.3% and 6.0% greater chances of having multimorbidity, respectively, than do those in good health. Severe self-esteem increases the likelihood of multimorbidity by 11.1%, while moderate sleep disturbances increase this likelihood by 5.7%. Young men aged 20-24 and 25-29 years had 4.4% and 2.5% greater likelihoods, respectively, than did those in the reference group, and moderate consumption of ultra-processed foods increased this likelihood by 2.1%. Conversely, men from the northern and northeastern regions were 5.0% and 4.0%, respectively, less likely to experience multimorbidity than were those from the southern region. Additionally, individuals in elementary, skilled, or administrative jobs are less likely to have multimorbidity than their professional counterparts are. Individuals who consumed alcohol and watched television moderately were also less likely to experience multimorbidity.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAn increasing number of adolescents and early-stage adults are affected by multiple long-term conditions. Most related research has focused on older adults, leaving a gap in understanding of multimorbidity in younger populations. This is crucial, as multimorbidity significantly impact health outcomes and quality of life.\u003c/p\u003e\n\u003cp\u003eThis study examined multimorbidity among Brazilian youth aged 15-29 years and reported an overall prevalence of 8.2%, which increases with age and is greater in women. There was a prevalence rate of 9.4% in women aged 15-29 years, nearly double that in men (4.8%). These findings align with recent Brazilian studies that reported similar rates for individuals aged 18-29 [18,19].\u003c/p\u003e\n\u003cp\u003eGlobally, the prevalence of MLTC among the young population varies widely due to differences in chronic condition definitions and age groupings. For instance, a 2020 study in England reported rates of 0.9% for those aged 0-19 and 5.9% for those aged 20-49 [34]. In Ontario, Canada, a 2016 study reported rates of 3.8% for women aged 0-17 years and 17.9% for those aged 18-44 years, while male rates were 5.0% and 14.8%, respectively [35].\u003c/p\u003e\n\u003cp\u003eExamining 14 chronic diseases, the study revealed that the most common combination of multimorbidity was depression with other mental disorders, affecting approximately 20% of men and women. Anxiety\u0026mdash;other mental disorders\u0026mdash;often observed in early adulthood\u0026mdash;can lead to mixed depressive and anxiety disorder (MDAD), which particularly affects younger individuals and women. The World Health Organization (WHO) recognized MDAD among the ICD-11 codes in 2018, with prevalence rates in primary care ranging from 1.8% to 11% [36,37]. These findings are consistent with previous research showing that depression rates are greater among those aged 14-22 years, especially among young women. Furthermore, depression frequently coexists with anxiety disorders, with a lifetime depression risk of 20% to 70% for these patients. Experiencing both conditions during adolescence can lead to significant stress and consequences such as school dropout or work-related issues [38-40].\u003c/p\u003e\n\u003cp\u003eThe study also revealed a link between depression and chronic diseases such as asthma, high cholesterol, and hypertension in both sexes. This finding aligns with prior research showing that depression often coexists with other conditions, especially when it starts in adolescence or early adulthood [41-43]. These findings underscore the need for public health initiatives to address the mental health crisis among youth. Early symptoms of depression and anxiety are significant risk factors for developing multiple health issues in middle age. Thus, it is crucial to consider these mental disorders in preventive strategies and integrated care to reduce chronic conditions later in life [41].\u003c/p\u003e\n\u003cp\u003eThe study showed that young people in Brazil\u0026apos;s northern, northeastern, and central western regions are less likely to experience multimorbidity than are those in the southern region. These regional disparities are possibly due to socioeconomic and health inequalities. For instance, the number of doctors per 1,000 inhabitants is three times greater in the southern region than in the northern region and double that in the northeastern and central western regions [44]. Additionally, young people in the southern region are three times more likely to have health insurance. These differences can lead to underdiagnosis in disadvantaged areas and overdiagnosis in wealthy regions, increasing the gap in the MLTC prevalence among different socioeconomic groups. This finding aligns with previous research suggesting that income can be positively associated with chronic diseases and multimorbidity in some low- and middle-income countries (LMICs) [14,23,24,28].\u003c/p\u003e\n\u003cp\u003eFrom a gender perspective, the study identified poor and fair self-perceived health, moderate and severe self-esteem issues and severe sleep disturbances as relevant risk factors for MLTC in both genders. However, the risk of multimorbidity associated with these factors was greater in young women, except for self-esteem issues, which were more common in men. Notably, factors such as education level, ethnicity, physical activity, urban residence, working conditions and smoking were not significant for either sex.\u003c/p\u003e\n\u003cp\u003eThe association between multimorbidity and self-rated health (SRH) was particularly strong among women with poor health, who had a 28.4% greater likelihood of having MLTC than did those in good health; for young men, this risk was 13.3%. Research indicates that SRH is a reliable indicator of future mental and physical health, and our findings confirm that poor SRH in adolescents increases the risk of MLTC in early adulthood, reflecting adaptation to long-term stress during this critical developmental period [45,46].\u003c/p\u003e\n\u003cp\u003eThis study highlights that moderate to severe self-esteem is a significant risk factor for MLTC in both genders and particularly affects young men. Self-esteem, as noted by Huitt (2009), reflects an individual\u0026apos;s sense of value and confidence [47]. Adolescence and young adulthood are crucial periods for psychological development and often coincide with a higher risk of mental disorders, especially among those with physical illnesses, leading to physical-mental multimorbidity [48,49].\u003c/p\u003e\n\u003cp\u003eOur research identified physical-mental multimorbidity combinations, such as asthma and depression, or triad combinations, such as asthma, depression, and other mental disorders. Decreased self-esteem may mediate these associations, which vary by sex. Adolescents with low self-esteem face long-term challenges, including being NEET (not employed, educated, or trained), financial difficulties, or criminal behavior. A Danish study revealed that young people aged 14 to 26 with physical-mental multimorbidity experience more psychosocial challenges and health risk behaviors than do those with only physical conditions [50].\u003c/p\u003e\n\u003cp\u003eSleep is vital for well-being and mental health, especially for adolescents who need 8 to 10 hours each night [51]. Insufficient sleep can lead to behavioral changes, academic challenges, weight gain, and mood issues [51,52]. The study showed that young women are more likely to experience multimorbidity than young men are, regardless of sleep disturbance severity. The risk of multimorbidity also increases with the severity of sleep issues in both genders. These findings highlight the association between sleep disturbances and adverse physical and mental health outcomes, along with an increase in chronic conditions and cognitive impairments among those affected [52-54].\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. Self-reported chronic diseases may cause recall bias, particularly among illiterate individuals and those with lower education levels. To address this, only participants who could respond independently were included. The cross-sectional design limits our understanding of causality and may introduce bias from unmeasured confounding factors. Additionally, while the study examined multiple chronic conditions based on 14 diseases prevalent in Brazil, it excluded certain disorders, including autism spectrum disorders and eating disorders, which are common in adolescents and young adults. The varying age ranges analyzed also complicate consistent comparisons.\u003c/p\u003e\n\u003cp\u003eDespite these limitations, the study has several strengths. The use of nationally representative data allows for the generalization of findings, and a larger sample size improves the accuracy of the results. The use of a parsimonious model yields reliable estimates, and the AME approach aids interpretation. Separate analyses by sex provide a more detailed understanding of the relationships between multimorbidity and potential predictors in Brazilian youth.\u003c/p\u003e\n\u003cp\u003eFuture research in Brazil should focus on (1) developing a framework to understand MLTC in childhood and adolescence, including definitions and patterns of physical and mental chronic conditions, their evolution, and severity; (2) longitudinal studies with clinical data to accurately track diagnoses, disease severity, and progression over time; (3) investigating the impact of socioeconomic inequalities on the risk of MLTC in adolescents and young adults; and (4) examining how specific combinations of chronic diseases determining MLTC impact healthcare expenditures to evaluate the effectiveness of National Health System (SUS) policies in reducing inequalities.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study revealed a significant association between chronic physical illnesses and mental health disorders in young people with MLTC, with varying influences based on sex. For women, particular risk factors include excessive computer use at home, having health insurance, and belonging to higher income brackets. For young men, moderate consumption of ultra-processed foods is particularly concerning. Additionally, regional protective factors are stronger for women, while working conditions tend to favor men. The findings highlight the importance of psychosocial and socioeconomic factors in determining the risk of MLTC, emphasizing the need for public policies that promote health and address social inequalities among young people.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors utilized AI-Grammarly to enhance the manuscript\u0026rsquo;s grammar and language understanding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e: The authors declare no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This work is not supported by any external funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e: The authors utilized AI-Grammarly to enhance the manuscript\u0026apos;s grammar and language clarity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e Our study was exempted from institutional ethics committee approval because it used publicly available secondary data. The 2019 National Health Survey was submitted to the National Research Ethics Committee/National Health Council and approved by Opinion No. 3,529,376, issued on August 23, 2019.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e: Open data from the public domain were used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e: \u0026nbsp;Pedro Olivares-Tirado contributed to the writing-original draft, conceptualization, formal analysis, investigation, methodology, project administration and edition. Rosendo Zanga contributed to the formal analysis, investigation, methodology and editing. Julieta Ar\u0026aacute;nguiz-Ram\u0026iacute;rez contributed to the investigation, methodology, project administration and editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eThe King\u0026apos;s Fund. Long-term conditions and multimorbidity. 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Insomnia and other sleep disorders in adolescence:\u0026nbsp;BMJ Pediatrics Open.2024;8:e001229. https://doi.org/10.1136/bmjpo-2021-001229\u003c/li\u003e\n \u003cli\u003eShort MA,\u0026nbsp;Gradisar M,\u0026nbsp;Lack LC,\u0026nbsp;et al. The impact of sleep on adolescent depressed mood, alertness and academic performance.\u0026nbsp;J Adolesc. 2013;\u0026nbsp;36:1025\u0026ndash;33. https://doi.org/10.1016/j.adolescence.2013.08.007\u003c/li\u003e\n \u003cli\u003eZhou Y, Jin Y, Zhu Y, Fang W, Dai X, Lim C, Mishra SR, Song P, Xu X. Sleep Problems Associate With Multimorbidity: A Systematic Review and Meta-analysis. Public Health Rev.2023; 44:1605469. https://doi.org/10.3389/phrs.2023.1605469.\u003c/li\u003e\n \u003cli\u003eHertenstein E, Feige B, Gmeiner T, Kienzler C, Spiegelhalder K, Johann A, et al. Insomnia as a Predictor of Mental Disorders: A Systematic Review and Meta-Analysis. Sleep Med Rev. 2019; 43:96\u0026ndash;105. https://doi.org/10.1016/j.smrv.2018.10.006\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Adolescent, Young adult, Multimorbidity, Multiple long-term conditions, Gender differences, Brazil","lastPublishedDoi":"10.21203/rs.3.rs-7368586/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7368586/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eRecent studies have highlighted the growing trend of multiple long-term conditions (MLTC) in younger people due to unhealthy lifestyles and various environmental stressors. This study aimed to investigate the effects of demographic, socioeconomic, health, and lifestyle factors on MLTC risk in Brazilian youth, with a particular focus on sex differences.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUsing a cross-sectional approach, this study used data from the 2019 Brazilian National Health Survey to analyze MLTC in people aged 15\u0026ndash;29 years. It examines 14 self-reported chronic conditions and independent variables, such as sociodemographic, health and lifestyle behaviors. Multivariate logistic regressions were used to identify factors and average marginal effects were employed to estimate the risk of multimorbidity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAccording to a survey of 17,708 young people, the overall MLTC prevalence was 8.02%, which was greater than that in young women (9.7%). Depression and mental disorders linked to asthma, hypercholesterolemia and hypertension were common. The key predictor factors included self-perceived health, self-esteem, and sleep disturbances. Regional socioeconomic disparities can mask problems of under- and overdiagnosis of the diseases analyzed. Education and physical activity were not significant factors in this study.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIncreasing rates of multimorbidity in young people pose significant challenges for healthcare systems and society. Chronic conditions such as mixed depressive and anxiety disorders, along with physical-mental comorbidities, can result in psychosocial issues, health risks, poor quality of life, and premature death. Therefore, it is essential to gain a better understanding of MLTC in youth to effectively prevent chronic diseases early in life, particularly in LMICs.\u003c/p\u003e","manuscriptTitle":"Risk factors for multimorbidity in adolescents and young adults in Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-18 17:36:51","doi":"10.21203/rs.3.rs-7368586/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-05T13:12:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-13T15:03:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46699627656632735549750297833734589678","date":"2025-11-09T17:24:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117112868271496303407181744723590468513","date":"2025-11-03T16:06:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-03T15:03:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14892075226623875274763563985919882124","date":"2025-09-16T08:06:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253597727394281757090139071112431578857","date":"2025-09-15T14:18:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-11T00:56:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-01T18:08:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-30T22:46:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2025-08-30T22:41:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7650026f-baf7-496f-9f59-fe944a7db701","owner":[],"postedDate":"September 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:00:12+00:00","versionOfRecord":{"articleIdentity":"rs-7368586","link":"https://doi.org/10.1186/s12982-026-01458-4","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2026-02-03 15:57:00","publishedOnDateReadable":"February 3rd, 2026"},"versionCreatedAt":"2025-09-18 17:36:51","video":"","vorDoi":"10.1186/s12982-026-01458-4","vorDoiUrl":"https://doi.org/10.1186/s12982-026-01458-4","workflowStages":[]},"version":"v1","identity":"rs-7368586","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7368586","identity":"rs-7368586","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}