{"paper_id":"424c9256-4281-4ccd-a97e-23513b62ea29","body_text":"Depression among people who live in coastal hazard areas in Indonesia: Evidence from a population-based national survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Depression among people who live in coastal hazard areas in Indonesia: Evidence from a population-based national survey Asri Maharani, Sujarwoto Sujarwoto, Herni Susanti, Helen Brooks, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4442319/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Climate change has a profound impact on the mental health and well-being of people all over the world. However, studies on the impacts of climate-driven rising sea levels on mental health remain few. This study aims to examine the risk of depression among people who live in coastal areas susceptible to the natural hazards associated with climate change. We used the Indonesia Basic Health Survey 2018, which included 642,419 adults in Indonesia. Multivariable logistic regression analysis was conducted to examine the relationship between living in a coastal hazard area and depression. We included socio-demographics, health status, and health access information in the analysis to identify the most vulnerable groups. Our findings show that people who live in coastline hazard areas are 1.13 times more likely to have depression than people who live outside those areas. Individuals living in the coastal hazards areas who were less likely to have autonomous mobility or resources, including young adults, females, those with low socio-economic conditions, and those with pre-existing health conditions, had a higher risk of depression than other groups. Culturally acceptable and effective mental health interventions should thus target these vulnerable populations and settings to effectively reduce climate-related health risks. Figures Figure 1 Figure 2 Introduction Climate change is widely regarded as a contributing factor to a growing number of worldwide emergencies, which have a significant effect on the impacted population’s mental health and well-being. 1 Extreme weather due to climate change has caused 2.1 million deaths and globally cost $ 4.3 trillion in economic losses in the past five decades. 2 Evidence of the consequences of climate change on mental health and well-being has been accumulating. 3 4 Prior studies have shown that exposure to climate-related stressors, such as heat, humidity, rainfall, drought, wildfires, and floods, have been linked to increased psychiatric hospitalisations, 5 suicide rates, 6 and psychological distress, 7 in addition to worsening mental health and higher mortality among individuals with pre-existing mental health conditions. 8 9 Without action, these burdens will become greater over the coming decades. Among the most significant consequences of climate change is rising sea levels. The global mean sea level has increased by about 20 cm since 1900. 10 The rise of sea level has been accelerating during the 20th and early 21st centuries, and it has increased at a rate of roughly 3.6 mm every year from 2006 to 2015. 11 Over 600 million people worldwide reside in low-lying coastal regions at a height of less than 10 metres. By 2050, the number of people living in these areas is anticipated to surpass 1 billion. 12 Sea level rise is predicted to exacerbate coastal erosion, catastrophic marine flooding, and saltwater intrusion in coastal aquifers. 13 It has further heightened the impact of hurricanes in coastal areas. 14 Sea level rise increases the risk of many forms of bodily harm: injury or drowning when coupled with extreme weather, infectious diseases from increased exposure to waterborne or vector-borne pathogens, health effects from increased exposure to contaminants or airborne pollutants, and numerous negative effects on social determinants of health. It is thus also important to understand the current and future mental health consequences of sea level rise in those communities most vulnerable to it. In this study, we examine areas affected by sea level rise to enable the development of targeted resilience building and intersectoral mitigation strategies. Recent studies have begun responding to the challenges presented by sea level rise in affected locations. In a study in the Solomon Islands, almost all (56 out of 57) respondents stated that they and their families were being impacted by sea level rise, which was creating anxiety and panic both personally and throughout the community. 15 Kelman et al. (2020) reported that populations in small island developing states experienced major adverse effects on mental health and well-being linked to climate change, including acute stress, anxiety, depression, and post-traumatic stress disorder (PTSD). 16 A study in two counties in Florida, USA, revealed that sea level rise and tropical cyclones were associated with a high risk of PTSD, anxiety, and major depressive disorder. However, these studies focused on limited geographical areas or selected islands and atolls and used community or volunteer samples rather than national ones. Hence, their findings cannot be widely generalised. This study addresses the above-mentioned gap by combining two nationally representative datasets from Indonesia. As the world’s largest archipelagic country, Indonesia’s coastal area is vulnerable to climate change. Indonesia ranks fifth in the world in terms of residents living in lower-elevation coastal zones, and without adaptation, the total population at risk of permanent flooding by 2070–2100 could exceed 4.2 million people. 17 Climate change has altered the characteristics of Indonesia’s coastline, including a decline in the natural coastline and an increase in the artificial coastline. 18 Over the last fifteen years, Indonesia has lost 29,261 hectares of its coastline—an area about the size of Jakarta—while natural sedimentation creates 895 hectares of new coastal land annually. According to a Ministry of Maritime Affairs and Fisheries report, erosion is causing Indonesia to lose some 1,950 hectares of coastal land annually. 19 Furthermore, floods, the possibility of irreversible flooding, and the incursion of saltwater into freshwater resources could render other locations uninhabitable. 20 Prior studies have revealed the increasing health risks, including vector-borne diseases, tuberculosis, diarrhoea, and skin diseases, due to climate change among coastal populations in two areas in Indonesia: Semarang 21 and Manado. 22 Certain groups are more vulnerable to the health effects of climate change than others because of social and economic factors such as income, education, access to health care, and housing. Identifying these groups is crucial in designing interventions to tackle the burden of mental disorders in areas affected by climate change. However, there is no evidence of the mental health burden in coastal areas affected by the rise in sea level in Indonesia. This study thus aims to: 1. Examine the consequences of living in areas affected by coastal hazards on the risk of depression. 2. Evaluate the effects of coastal hazards, i.e., abrasions, hurricanes, and tidal flooding, on depression. 3. Identify groups within the population living in areas with coastal hazards at higher risk of depression. Study design and method Study design This study used a cross-sectional design with data from the most recent Indonesia Basic Health Survey ( Riset Kesehatan Dasar or Riskesdas ) 2018. 23 Riskesdas is a nationally representative survey conducted every five years in all 34 provinces and 514 districts of Indonesia by the National Institute of Health Research and Development (NIHRD), Ministry of Health; it focuses on the measurement of health indicators mandated by the Millennium Development Goals or Sustainable Development Goals, including mental health and non-communicable diseases. The Ethics Committee of the NIHRD provided ethical clearance before data collection. Because we conducted secondary data analyses, the pre-requisite of ethics approval is not applicable. Prior to the data collection and before the interviews were conducted, the enumerators obtained informed consent from the respondents in the form of written consent. Participants were selected using a multistage systematic random sampling method. The first stage identified groups of census blocks and designated them as primary sampling units (PSUs). The second stage used a probability proportional sampling method to design the enrollment size to identify a census block from each PSU. The third comprised systematic random sampling of 25 census buildings from each block. One household from each census building was randomly chosen in the fourth stage. All household members (defined as those having stayed on the premises for the past six months or more and having the same financial source for food) of each selected household were asked to participate in the survey. The study sample focuses on adults aged 18 years and older who had completed information on mental health depression questions. The total number of respondents whose information was used in the analyses was 642,419. The Riskesdas 2018 data was linked to the Village Survey ( Potensi Desa or Podes) data in the same year (2018). Podes provides information on potential assets that belong to the smallest area unit in Indonesia, i.e. the village, including its social economy, infrastructure, and human resources. For this study, we retrieved information on whether the village is situated in a coastal area and has experienced abrasion, hurricanes, and/or tidal flooding in the past three years. We aggregated the data at the district level and supplemented them with individual data from Riskesdas 2018 using district codes from Indonesia Statistics. Taken together, these data capture the nested structure of individuals within districts. Ethical approval for Riskesdas was obtained via the Ethical Commission of the National Institute of Health Research and Development, Ministry of Health, the Republic of Indonesia (No. LB.02.01/2/KE.267/2017). Participants gave informed consent to participate in the study before taking part. Procedures were performed in accordance with national guidelines and regulations for research activities. Measures of depression Depression was assessed using the Mini-International Neuropsychiatric Interview (MINI) version 6 of the Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV and the International Classification of Diseases − 10, a structured diagnostic psychiatric interview used to assess various mental health problems. 24–26 It is widely used in clinical and research settings and has been translated into various languages. A previous study validated the Indonesian version of the MINI depression section. 27 The questionnaire consists of ten questions with “yes” or “no” answers. A respondent was classed as depressed if they answered “yes” to at least two of the questions numbered 1 to 3 and “yes” to at least two of the questions numbered 4 to 10. Measures of coastal hazards We categorised the district as a coastal area if it has villages immediately on the coastline. We classified the respondents as living in an area with coastal abrasion, living on a coastline with hurricanes, and having regular experience with tidal flooding if they lived in a district having experienced those disasters in the past three years. Covariates We classified individual-level risk factors of depression as demographic, socio-economic, health behaviour-related, health status-related, and household characteristic-related. The demographic factors included age group (18–24 as reference, 25–34, 35–44, 45–54, 55–64, 65–74, 75+) and sex (male as reference). Marital status was categorised as single (reference), married, or divorced/widowed. The socio-economic factors included the highest attained educational level (primary school or less, junior high school, and senior high school and higher as reference), household expenditure per month in quintiles, and occupation (unemployed as reference, student, employed/retired, self-employed, informal worker, and other). Smoking, alcohol use and physical activity were included as indicators of health behaviour determinants. We categorised smoking behaviour into smoking every day (reference), not every day, past smoker and non-smoker. Similarly, alcohol use was classified as drinking alcohol under standard (reference), more than standard, or never drinking alcohol. The presence of chronic diseases was measured using self-reports of ever having been diagnosed by a health professional as suffering from tuberculosis, hypertension, stroke, diabetes mellitus, heart disease, asthma, rheumatoid arthritis, cancer, or renal failure. Principal component analysis (PCA) was used to determine the degree of difficulty in accessing healthcare services based on three dimensions of information: ( 1 ) the modes of transportation used to access healthcare services; ( 2 ) the round-trip time from the home to the healthcare provider; and ( 3 ) the round-trip transportation cost to the healthcare provider. 28 We divided the challenges associated with reaching healthcare providers into two categories: easy (score ≥ mean/median) and difficult (score < mean/median). Statistical analyses We analysed the data in several steps. Firstly, we created maps to illustrate the importance of residential areas with regard to coastal hazards and the prevalence of depression across 514 districts in Indonesia. Secondly, we described the characteristics of the respondents in terms of residential area and the presence of depression. We presented the odds ratios (ORs) and significance of the difference by implementing the alpha of 5% error through χ2 analysis for the covariates with two categories and binary logistic regression for the covariates with more than two categories. Finally, multivariable logistic regression analysis was conducted to examine the relationship between living in a coastal hazard area and the risk of depression. We performed the multivariable logistic regression for all samples and respondents living in areas with all three types of coastal hazards combined and separately for those living in areas with abrasion, hurricanes, and tidal floods. We calculated marginal effects to estimate the predicted probability of having depression with healthcare access and household expenditure as outcome variables. We used STATA 18.0 for this analysis, considering the corresponding Riskesdas weights, strata, and primary sampling unit according to its survey design. Results Geographical distribution of SLR and depression Figure 1 describes the geographical distribution of abrasion, hurricanes, and tidal flooding across districts in Indonesia. The map shows the variations in occurrence of these natural hazards, with the highest number found across the northern and southern Java coastlines, northeastern and southern Sumatra coastlines, West Sumatra coastline, northwest Sulawesi coastline, southern Papua coastline, and on the small islands of southern Maluku and eastern Nusa Tenggara. More than one hundred of the coastline hazard areas are found on those islands. The geographical variation in the prevalence of depression across districts is also apparent (Fig. 2 ). People living across the northern and southern Java coastlines, northeastern Sumatra, West Sumatra and the southeastern Sumatra coastline, northwestern Sulawesi coastline, southern Papua coastline, and on the small islands of southern Maluku and eastern Nusa Tenggara had the highest prevalence of depression with 20–30% of their populations having depression. Characteristics of respondents The first four columns of Table 1 describe the characteristics of respondents’ socio-demographic and depression status based on living area (coastal or non-coastal). A detailed description of respondents based on types of coastal hazards (abrasion, hurricane, and tidal flood exposure) is presented in Supplementary Tables S1 and S2 . The percentage of respondents who lived in coastal hazard areas was 31.3%. The prevalence of depression among respondents living in coastal hazard areas (6.7%) was higher than among those living outside coastal hazard areas (5.7%). The highest prevalence of depression was found among people living with tidal flood exposure (7.5%). The age distributions in the two types of areas were relatively similar, with 23.5% being in the 35–44 age group. In both types of areas, the proportion of females was slightly higher than males. The percentage of respondents educated at the primary school level or less was higher among those living in coastal hazard areas (48.8%) than those living outside (43.5%). The highest proportion of people with a low educational level was found among those living in tidal flood areas (51.4%). Likewise, the percentage of respondents working in informal sectors within coastal hazard areas was higher (37.4%) than those living outside (33.8%). The highest percentage of informal workers was found among people living in tidal flood areas (40.0%). The proportion of families within the first quintile of expenditures within coastal hazard areas was also larger (20.9%) than those outside (16.4%). The highest percentage of low-income families was located in coastal areas frequently facing tidal floods (23.0%). People living in coastal hazard areas had less access to healthcare than their counterparts, as 14.1% of respondents living in those areas reported difficulty accessing healthcare compared with 10.1% of those living outside them. The highest percentage of respondents facing difficulty accessing healthcare appeared in coastal areas frequently facing tidal floods (15.8%). Smoking was slightly more prevalent among people living in coastal hazard areas (27.2%) than 26.8% of those living outside coastal hazards. The highest percentage of smokers was found among people living in tidal flood areas (28.5%). Likewise, the proportions of those using alcohol both under and above standard were larger among people living in coastal hazard areas (3.3% and 3.2%, respectively) than those living outside coastal hazard areas (2.5% and 1.4%, respectively). People living in coastline areas were also less likely to engage in physical activity (10.1%) than those living outside coastline areas (9.8%). The highest percentage of people with less physical activity was found among people living in tidal flood areas (9.4%). The most common illnesses among people living in coastal hazard areas were hypertension (8.7%) and rheumatoid arthritis (8.0%), while the least common was cancer (0.3%). The last three columns of Table 1 show the characteristics of respondents living in areas experiencing coastal hazards with respect to the presence of depression. We found the highest percentage of depression among respondents living in coastal hazard areas within the 35–44 age group (22.1%), females (62.7%), those with primary school or less education (69.7%), informal workers (37.4%) and unemployed people (37.0%), those in the first quintile of expenditures (25.5%), and those with difficulty to access healthcare (20.8%). These socio-demographic characteristics were similar among people living in areas where abrasion, hurricanes, and tidal flooding often occur, with a slightly higher percentage found among people living in tidal flooding areas. About one-fifth of respondents living in coastal hazard areas with depression smoked every day (23.4%). The highest percentage of smokers was found among depressed people living in tidal flooding areas (24.6%). The percentage of alcohol use was higher among respondents living in coastal hazard areas with depression than those without depression. One-tenth of these reported having little physical activity. Among depressed people, 14.7% had rheumatoid arthritis, and 14.2% had hypertension. Multivariable logistic regression Table 2 describes the results of the multivariable logistic regression. Respondents living in coastal hazard areas were 1.13 more likely to have depression than those living outside them. Likewise, respondents living in areas with coastal abrasions were 1.16 times more likely to have depression than those living outside those areas. Those living in coastal areas with hurricanes were 1.14 times more likely to have depression than their counterparts. Those living in coastal areas with tidal flooding were 1.24 times more likely to have depression. In all models, young adults (18–24 years) were more likely to experience depression than members of other age groups. Females were 2.16–2.17 times more likely to experience depression than males. Those who graduated from junior high school and those with a primary school education or less were more likely to have depression than those who graduated from senior high school. Married people were less likely to have depression than single people. The odds of being depressed for divorced and widowed individuals were 1.12–1.13 times greater than those of single individuals. In all models, jobless individuals were more likely to have depression than employed individuals. Tabel 1 Characteristics of all respondents based on residential area (with or without coastal hazards) and of respondents living in coastal hazard areas by presence of depression Variables All respondents Respondents living in coastal hazard areas (N = 201,109) Non-coastal hazard areas (N = 441,310) Coastal hazard areas (N = 201,109) p-value Not depressed (N = 187,548) Depressed (N = 13,561) p-value Depression No 416,256 (94.3%) 187,548 (93.3%) < 0.001 Yes 25,054 (5.7%) 13,561 (6.7%) Age 18–24 years old 59,746 (13.5%) 26,962 (13.4%) < 0.001 25,133 (13.4%) 1,829 (13.5%) < 0.001 25–34 years old 90,473 (20.5%) 41,953 (20.9%) 39,616 (21.1%) 2,337 (17.2%) 35–44 years old 104,336 (23.6%) 47,358 (23.5%) 44,361 (23.7%) 2,997 (22.1%) 45–54 years old 88,343 (20.0%) 39,594 (19.7%) 36,818 (19.6%) 2,776 (20.5%) 55–64 years old 58,999 (13.4%) 26,188 (13.0%) 24,269 (12.9%) 1,919 (14.2%) 65–74 years old 26,677 (6.0%) 12,909 (6.4%) 11,808 (6.3%) 1,101 (8.1%) 75 years old and above 12,736 (2.9%) 6,145 (3.1%) 5,543 (3.0%) 602 (4.4%) Gender Male 210,023 (47.6%) 94,678 (47.1%) < 0.001 89,618 (47.8%) 5,060 (37.3%) < 0.001 Female 231,287 (52.4%) 106,431 (52.9%) 97,930 (52.2%) 8,501 (62.7%) Education Senior high school or higher 169,555 (38.4%) 70,420 (35.0%) < 0.001 67,044 (35.7%) 3,376 (24.9%) < 0.001 Junior high school 79,851 (18.1%) 32,617 (16.2%) 30,505 (16.3%) 2,112 (15.6%) Primary school or lower 191,904 (43.5%) 98,072 (48.8%) 89,999 (48.0%) 8,073 (59.5%) Marital status Unmarried 68,421 (15.5%) 30,423 (15.1%) < 0.001 28,417 (15.2%) 2,006 (14.8%) < 0.001 Married 329,056 (74.6%) 151,013 (75.1%) 141,564 (75.5%) 9,449 (69.7%) Divorced or widowed 43,833 (9.9%) 19,673 (9.8%) 17,567 (9.4%) 2,106 (15.5%) Employment status Jobless 128,695 (29.2%) 57,798 (28.7%) < 0.001 52,780 (28.1%) 5,018 (37.0%) < 0.001 Student 12,936 (2.9%) 5,840 (2.9%) 5,404 (2.9%) 436 (3.2%) Employed or retired 56,378 (12.8%) 21,946 (10.9%) 21,165 (11.3%) 781 (5.8%) Self-employed 66,687 (15.1%) 24,635 (12.2%) 23,398 (12.5%) 1,237 (9.1%) Informal worker 149,199 (33.8%) 75,227 (37.4%) 70,158 (37.4%) 5,069 (37.4%) Other 27,415 (6.2%) 15,663 (7.8%) 14,643 (7.8%) 1,020 (7.5%) Monthly household expenditure by quintile 1st quintile 72,460 (16.4%) 41,935 (20.9%) < 0.001 38,480 (20.5%) 3,455 (25.5%) < 0.001 2nd quintile 80,993 (18.4%) 40,228 (20.0%) 37,243 (19.9%) 2,985 (22.0%) 3rd quintile 87,415 (19.8%) 39,936 (19.9%) 37,237 (19.9%) 2,699 (19.9%) 4th quintile 94,449 (21.4%) 40,326 (20.1%) 37,866 (20.2%) 2,460 (18.1%) 5th quintile 105,993 (24.0%) 38,684 (19.2%) 36,722 (19.6%) 1,962 (14.5%) Have difficulty accessing healthcare No 361,986 (89.9%) 150,424 (85.9%) < 0.001 141,514 (86.4%) 8,910 (79.2%) < 0.001 Yes 40,706 (10.1%) 24,654 (14.1%) 22,320 (13.6%) 2,334 (20.8%) Smoking status Every day 118,580 (26.9%) 54,712 (27.2%) < 0.001 51,538 (27.5%) 3,174 (23.4%) < 0.001 Not every day 20,187 (4.6%) 9,706 (4.8%) 9,031 (4.8%) 675 (5.0%) Former smoker 24,107 (5.5%) 9,630 (4.8%) 8,831 (4.7%) 799 (5.9%) Never smoked 278,436 (63.1%) 127,061 (63.2%) 118,148 (63.0%) 8,913 (65.7%) Alcohol use Under standard 11,145 (2.5%) 6,685 (3.3%) < 0.001 6,121 (3.3%) 564 (4.2%) < 0.001 More than standard 6,261 (1.4%) 6,420 (3.2%) 5,873 (3.1%) 547 (4.0%) No alcohol 423,904 (96.1%) 188,004 (93.5%) 175,554 (93.6%) 12,450 (91.8%) Physical activity Less active 43,399 (9.8%) 20,216 (10.1%) 0.007 18,743 (10.0%) 1,473 (10.9%) 0.001 Active 397,911 (90.2%) 180,893 (89.9%) 168,805 (90.0%) 12,088 (89.1%) Have you ever been diagnosed with lung tuberculosis? No 439,019 (99.5%) 200,118 (99.5%) 0.169 186,715 (99.6%) 13,403 (98.8%) < 0.001 Yes 2,291 (0.5%) 991 (0.5%) 833 (0.4%) 158 (1.2%) Have you ever been diagnosed with hypertension? No 401,688 (91.0%) 183,702 (91.3%) < 0.001 172,063 (91.7%) 11,639 (85.8%) < 0.001 Yes 39,622 (9.0%) 17,407 (8.7%) 15,485 (8.3%) 1,922 (14.2%) Have you ever been diagnosed with stroke? No 435,842 (98.8%) 198,781 (98.8%) 0.006 185,669 (99.0%) 13,112 (96.7%) < 0.001 Yes 5,468 (1.2%) 2,328 (1.2%) 1,879 (1.0%) 449 (3.3%) Have you ever been diagnosed with diabetes mellitus? No 430,876 (97.6%) 197,059 (98.0%) < 0.001 184,027 (98.1%) 13,032 (96.1%) < 0.001 Yes 10,434 (2.4%) 4,050 (2.0%) 3,521 (1.9%) 529 (3.9%) Have you ever been diagnosed with heart disease? No 432,384 (98.0%) 197,557 (98.2%) < 0.001 184,484 (98.4%) 13,073 (96.4%) < 0.001 Yes 8,926 (2.0%) 3,552 (1.8%) 3,064 (1.6%) 488 (3.6%) Have you ever been diagnosed with asthma? No 429,663 (97.4%) 195,512 (97.2%) < 0.001 182,765 (97.4%) 12,747 (94.0%) < 0.001 Yes 11,647 (2.6%) 5,597 (2.8%) 4,783 (2.6%) 814 (6.0%) Have you ever been diagnosed with rheumatoid arthritis? No 402,771 (91.3%) 184,910 (91.9%) < 0.001 173,339 (92.4%) 11,571 (85.3%) < 0.001 Yes 38,539 (8.7%) 16,199 (8.1%) 14,209 (7.6%) 1,990 (14.7%) Have you ever been diagnosed with cancer? No 440,088 (99.7%) 200,586 (99.7%) 0.229 187,113 (99.8%) 13,473 (99.4%) < 0.001 Yes 1,222 (0.3%) 523 (0.3%) 435 (0.2%) 88 (0.6%) Have you ever been diagnosed with kidney failure? No 439,435 (99.6%) 200,292 (99.6%) 0.284 186,860 (99.6%) 13,432 (99.0%) < 0.001 Yes 1,875 (0.4%) 817 (0.4%) 688 (0.4%) 129 (1.0%) Family member with psychosis No 437,596 (99.2%) 199,287 (99.1%) 0.010 185,991 (99.2%) 13,296 (98.0%) < 0.001 Yes 3,714 (0.8%) 1,822 (0.9%) 1,557 (0.8%) 265 (2.0%) The association of household expenditures with the risk of depression varied across respondent categories. In Models 1 and 2, the 2nd quintile of expenditure was not significantly associated with depression. In these models, households in the 3rd, 4th and 5th quintiles were less likely to have depression than those in the 1st quintile of expenditures. In Models 3 and 4, being in the 2nd and 3rd quintiles of household expenditure was not significantly associated with depression. In these models, households within the 4th and 5th quintiles were less likely to have depression than those within the 1st quintile. In all models, past smokers were more likely to have depression than regular smokers (OR = 1.24–1.25). In contrast, non-smokers were less likely to have depression than regular smokers (OR = 0.62). Those who reported not drinking alcohol were less likely to have depression than those who did consume alcohol. Respondents who engaged in physical activity were less likely to have depression than those who were less active. In all models, individuals with non-communicable diseases (lung tuberculosis, hypertension, stroke, diabetes mellitus, heart disease, asthma, rheumatoid arthritis, cancer, and kidney failure) were more likely to have depression than those without these illnesses. People with difficulty accessing healthcare were more likely to have depression (OR = 1.37). Table 3 describes the results of multivariable logistic regression for respondents living in coastal hazard areas in general and those living in coastal abrasion areas, coastal hurricane areas, and tidal flooding areas. No significant association with depression was found in the 35–44 age group, but other age groups showed a significant association with depression. The results highlight that young adults (18–24 years) were more likely to suffer from depression. In all categories of respondents living in coastal hazard areas, females were more likely to have depression than males. Those educated at the junior secondary level and those with a primary school education or less were more likely to have depression than those educated at the senior high school level or higher. Divorced and widowed respondents were 1.10–1.16 times more likely to have depression than unmarried respondents. Married respondents were less likely to have depression. In all categories of respondents living in coastal hazard areas, jobless respondents were more likely to have depression than employed and retired respondents. Table 2 Multivariable logistic regression results showing the associations between living in areas with coastal hazards and depression Model 1 Model 2 Model 3 Model 4 Living in area with coastal hazards 1.13 [1.10, 1.16]** Living in area with coastal abrasion 1.16 [1.13, 1.19]** Living in coastal area with hurricanes 1.14 [1.11, 1.16]** Living in area with tidal flooding 1.24 [1.20, 1.28]** Age group (Ref.: 18–24 years old ) 25–34 years old 0.88 [0.83, 0.92]** 0.88 [0.84, 0.92]** 0.88 [0.84, 0.92] 0.88 [0.84;0.92] 35–44 years old 0.92 [0.87, 0.96]** 0.92 [0.87, 0.96]** 0.92 [0.87, 0.96]** 0.92 [0.87;0.96]** 45–54 years old 0.90 [0.86, 0.95]** 0.90 [0.86, 0.95]** 0.90 [0.86, 0.95]** 0.90 [0.86;0.95]** 55–64 years old 0.74 [0.70, 0.78]** 0.74 [0.70, 0.78]** 0.74 [0.70, 0.78]** 0.74 [0.70;0.78]** 65–74 years old 0.71 [0.67, 0.76]** 0.72 [0.67, 0.76]** 0.71 [0.67, 0.76]** 0.72 [0.67;0.76]** 75 years old or above 0.68 [0.63, 0.73]** 0.68 [0.63, 0.74]** 0.68 [0.63, 0.73]** 0.68 [0.63;0.74]** Female 2.17 [2.08, 2.25]** 2.16 [2.08, 2.25]** 2.16 [2.08, 2.25]** 2.16 [2.08;2.25]** Education (Ref.: senior high school or higher) Junior high school 1.19 [1.15, 1.23]** 1.19 [1.15, 1.23]** 1.19 [1.15, 1.23]** 1.19 [1.15;1.23]** Primary school or lower 1.38 [1.34, 1.43]** 1.38 [1.34, 1.43]** 1.38 [1.34, 1.43]** 1.38 [1.34;1.43]** Marital status (Ref.: unmarried) Married 0.78 [0.75, 0.81]** 0.78 [0.75, 0.81]** 0.78 [0.75, 0.81]** 0.78 [0.75;0.81]** Divorced or widowed 1.13 [1.07, 1.19]** 1.13 [1.06, 1.19]** 1.13 [1.07, 1.19]** 1.12 [1.06;1.19]** Employment status (Ref.: jobless) Student 0.99 [0.92, 1.07] 1.00 [0.93, 1.07] 0.99 [0.92, 1.07] 0.99 [0.92;1.07] Employed or retired 0.66 [0.63, 0.70]** 0.67 [0.64, 0.70]** 0.66 [0.63, 0.70]** 0.67 [0.63;0.70]** Self-employed 0.82 [0.78, 0.85]** 0.82 [0.78, 0.85]** 0.82 [0.78, 0.85]** 0.82 [0.78;0.85]** Informal worker 0.84 [0.82, 0.87]** 0.84 [0.82, 0.87]** 0.84 [0.82, 0.87]** 0.84 [0.82;0.87]** Other 0.87 [0.83, 0.92]** 0.87 [0.83, 0.91]** 0.87 [0.83, 0.92]** 0.87 [0.83;0.92]** Monthly household expenditure (Ref.: 1st quintile) 2nd quintile 0.98 [0.95, 1.02] 0.98 [0.95, 1.02] 0.98 [0.95, 1.02] 0.98 [0.95;1.02] 3rd quintile 0.96 [0.93, 1.00]* 0.96 [0.93, 1.00]* 0.97 [0.93, 1.00]* 0.97 [0.93, 1.00]* 4th quintile 0.89 [0.86, 0.92]** 0.89 [0.86, 0.92]** 0.89 [0.86, 0.92]** 0.89 [0.86, 0.92]** 5th quintile 0.78 [0.75, 0.81]** 0.79 [0.76, 0.82]** 0.79 [0.76, 0.82]** 0.79 [0.76, 0.82]** Smoking status (Ref.: every day) Not every day 1.00 [0.94, 1.06] 1.00 [0.94, 1.06] 1.00 [0.94, 1.06] 1.00 [0.94, 1.06] Former smoker 1.24 [1.18, 1.31]** 1.25 [1.18, 1.31]** 1.24 [1.18, 1.31]** 1.25 [1.19, 1.32]** Never smoked 0.62 [0.60, 0.65]** 0.62 [0.60, 0.65]** 0.62 [0.60, 0.65]** 0.62 [0.60, 0.65]** Alcohol use (Ref.: under standard) More than standard 0.97 [0.89, 1.06] 0.97 [0.89, 1.06] 0.97 [0.89, 1.06] 0.97 [0.89, 1.06] No alcohol 0.55 [0.52, 0.59]** 0.55 [0.52, 0.58]** 0.55 [0.52, 0.59]** 0.55 [0.51, 0.58]** Physically active 0.90 [0.87, 0.94]** 0.91 [0.87, 0.94]** 0.90 [0.87, 0.94]** 0.90 [0.87, 0.94]** Ever diagnosed with lung tuberculosis 2.20 [1.97, 2.45]** 2.20 [1.97, 2.45]** 2.20 [1.97, 2.45]** 2.20 [1.97, 2.45]** Ever diagnosed with hypertension 1.42 [1.37, 1.47]** 1.42 [1.37, 1.47]** 1.42 [1.37, 1.47]** 1.42 [1.37, 1.47]** Ever diagnosed with stroke 2.45 [2.29, 2.62]** 2.45 [2.29, 2.62]** 2.45 [2.29, 2.62]** 2.45 [2.29, 2.62]** Ever diagnosed with diabetes 1.64 [1.54, 1.73]** 1.64 [1.54, 1.73]** 1.64 [1.54, 1.73]** 1.64 [1.54, 1.73]** Ever diagnosed with heart disease 1.57 [1.48, 1.67]** 1.57 [1.48, 1.67]** 1.57 [1.48, 1.67]** 1.58 [1.48, 1.67]** Ever diagnosed with asthma 2.02 [1.92, 2.13]** 2.02 [1.92, 2.13]** 2.02 [1.92, 2.13]** 2.03 [1.92, 2.13]** Ever diagnosed with rheumatoid arthritis 1.85 [1.79, 1.91]** 1.85 [1.79, 1.91]** 1.85 [1.79, 1.91]** 1.85 [1.79, 1.91]** Ever diagnosed with cancer 2.33 [2.02, 2.69]** 2.33 [2.02, 2.69]** 2.34 [2.02, 2.69]** 2.34 [2.02, 2.69]** Ever diagnosed with kidney failure 2.08 [1.85, 2.34]** 2.08 [1.85, 2.34]** 2.08 [1.85, 2.34]** 2.08 [1.85, 2.34]** Family member with psychosis 2.33 [2.14, 2.53]** 2.33 [2.14, 2.53]** 2.33 [2.14, 2.54]** 2.34 [2.14, 2.54]** Have difficulty accessing healthcare 1.37 [1.33, 1.42]** 1.37 [1.33, 1.42]** 1.37 [1.33, 1.41]** 1.37 [1.33, 1.41]** Intercept 0.11 [0.10, 0.12]** 0.11 [0.10, 0.12]** 0.11 [0.10, 0.12]** 0.11 [0.10, 0.12]** Number of observations 577770 577770 577770 577770 ** p < .01, * p < .05 Table 3 Multivariable logistics regression results showing factors associated with depression in areas with (1) coastal hazards, (2) coastal abrasions, (3) hurricanes, and (4) tidal flooding. Individuals living in areas with coastal hazards Individuals living in areas with coastal abrasion Individuals living in coastal areas with hurricane Individuals living in areas with coastal areas with tidal flooding Age group (Ref.: 18–24) 25–34 0.88 [0.81;0.95]** 0.86 [0.79;0.94]** 0.87 [0.80;0.95]** 0.91 [0.82;1.02] 35–44 0.94 [0.87;1.03] 0.93 [0.85;1.02] 0.94 [0.86;1.03] 0.98 [0.87;1.10] 45–54 0.95 [0.87;1.04] 0.95 [0.87;1.05] 0.95 [0.87;1.04] 1.00 [0.89;1.13] 55–64 0.76 [0.69;0.84]** 0.76 [0.69;0.85]** 0.77 [0.69;0.85]** 0.81 [0.71;0.93]** 65–74 0.73 [0.65;0.82]** 0.73 [0.65;0.83]** 0.74 [0.66;0.83]** 0.83 [0.72;0.97]* 75+ 0.72 [0.63;0.82]** 0.73 [0.63;0.85]** 0.72 [0.62;0.83]** 0.81 [0.67;0.97]** Female 2.03 [1.90;2.18]** 1.96 [1.81;2.11]** 2.05 [1.90;2.20]** 1.97 [1.79;2.16]** Education (Ref.: senior high school or higher) Junior high school 1.20 [1.12;1.28]** 1.22 [1.14;1.31]** 1.23 [1.15;1.31]** 1.29 [1.18;1.41]** Primary school or lower 1.39 [1.32;1.47]** 1.42 [1.34;1.51]** 1.41 [1.33;1.49]** 1.43 [1.33;1.55]** Marital status (Ref.: unmarried) Married 0.80 [0.74;0.86]** 0.79 [0.73;0.86]** 0.81 [0.75;0.87]** 0.81 [0.73;0.90]** Divorced or widowed 1.14 [1.04;1.26]** 1.10 [0.99;1.23] 1.12 [1.02;1.24]* 1.16 [1.02;1.32]* Employment status (Ref.: jobless) Student 1.06 [0.94;1.19] 1.01 [0.87;1.16] 1.03 [0.91;1.18] 1.10 [0.93;1.31] Employed or retired 0.65 [0.59;0.71]** 0.67 [0.60;0.74]** 0.65 [0.59;0.71]** 0.71 [0.62;0.80]** Self-employed 0.77 [0.72;0.83]** 0.81 [0.74;0.87]** 0.79 [0.73;0.86]** 0.86 [0.78;0.95]** Informal worker 0.85 [0.80;0.89]** 0.81 [0.77;0.86]** 0.86 [0.81;0.90]** 0.85 [0.79;0.91]** Other 0.87 [0.80;0.94]** 0.85 [0.78;0.93]** 0.89 [0.82;0.96]** 0.93 [0.84;1.03] Monthly household expenditure (Ref.: 1st quintile) 2nd quintile 0.93 [0.88;0.99]* 0.96 [0.90;1.02] 0.93 [0.88;0.99]* 0.95 [0.88;1.03] 3rd quintile 0.90 [0.85;0.95]** 0.92 [0.86;0.98]* 0.90 [0.85;0.96]** 0.93 [0.86;1.01] 4th quintile 0.82 [0.77;0.87]** 0.84 [0.78;0.90]** 0.84 [0.79;0.89]** 0.86 [0.79;0.94]** 5th quintile 0.76 [0.71;0.81]** 0.79 [0.74;0.86]** 0.77 [0.72;0.83]** 0.81 [0.74;0.89]** Smoking status (Ref: every day) Not every day 1.10 [1.00;1.22]* 1.10 [0.99;1.23] 1.08 [0.98;1.20] 1.11 [0.98;1.27] Former smoker 1.23 [1.12;1.35]** 1.22 [1.10;1.36]** 1.24 [1.13;1.37]** 1.15 [1.00;1.31]* Never smoked 0.66 [0.61;0.71]** 0.68 [0.63;0.74]** 0.65 [0.61;0.71]** 0.66 [0.60;0.73]** Alcohol use (Ref.: under standard) More than standard 0.99 [0.86;1.13] 0.95 [0.81;1.10] 0.99 [0.86;1.13] 0.90 [0.75;1.09] No alcohol 0.59 [0.54;0.66]** 0.59 [0.53;0.66]** 0.58 [0.52;0.64]** 0.57 [0.50;0.66]** Physically active 0.90 [0.85;0.96]** 0.90 [0.84;0.96]** 0.90 [0.84;0.96]** 0.93 [0.85;1.02] Ever diagnosed with lung tuberculosis 2.11 [1.73;2.56]** 2.22 [1.79;2.74]** 2.05 [1.66;2.52]** 2.22 [1.71;2.89]** Ever diagnosed with hypertension 1.35 [1.27;1.44]** 1.36 [1.27;1.46]** 1.36 [1.28;1.46]** 1.36 [1.25;1.49]** Ever diagnosed with stroke 2.50 [2.22;2.82]** 2.41 [2.10;2.76]** 2.41 [2.12;2.74]** 2.35 [1.97;2.80]** Ever diagnosed with diabetes 1.67 [1.51;1.86]** 1.77 [1.57;1.98]** 1.69 [1.51;1.89]** 1.84 [1.59;2.12]** Ever diagnosed with heart disease 1.66 [1.49;1.85]** 1.62 [1.43;1.83]** 1.65 [1.46;1.85]** 1.48 [1.26;1.74]** Ever diagnosed with asthma 2.06 [1.89;2.25]** 2.10 [1.91;2.31]** 2.10 [1.92;2.30]** 2.22 [1.97;2.50]** Ever diagnosed with rheumatoid arthritis 1.78 [1.68;1.89]** 1.80 [1.69;1.92]** 1.75 [1.65;1.87]** 1.71 [1.57;1.85]** Ever diagnosed with cancer 2.40 [1.88;3.08]** 2.64 [2.02;3.46]** 2.28 [1.73;2.99]** 2.62 [1.84;3.71]** Ever diagnosed with kidney failure 1.87 [1.51;2.31]** 2.11 [1.68;2.65]** 1.91 [1.52;2.38]** 2.03 [1.54;2.69]** Family member with psychosis 2.14 [1.85;2.49]** 2.13 [1.81;2.51]** 2.18 [1.86;2.56]** 2.10 [1.69;2.61]** Difficulty to access healthcare 1.45 [1.38;1.53]** 1.41 [1.33;1.49]** 1.45 [1.38;1.53]** 1.44 [1.35;1.54]** Intercept 0.11 [0.10;0.13]** 0.12 [0.10;0.13]** 0.11 [0.09;0.13]** 0.11 [0.09;0.13]** Number of observations 175078 138286 155488 84498 ** p < .01;* p < .05 The association of household expenditure with depression varied across the categories of residential areas. Within coastal abrasion areas, significant associations were found within the third to fifth quintiles, while in tidal flooding areas, significant associations were found within the fourth and fifth quintiles. The findings show that households within the poorest quintiles are more likely to have depression. In all categories of residential areas, ex-smokers were more likely to have depression than regular smokers (OR = 1.15–1.24). In contrast, non-smokers were less likely to have depression than regular smokers (OR = 0.66–0.68). Those who reported not drinking alcohol were less likely to have depression than those who consumed alcohol. Respondents who were physically active were less likely to have depression than those who were less active (OR = 0.90–0.93). In all models, individuals with lung tuberculosis, hypertension, stroke, diabetes mellitus, heart disease, asthma, rheumatoid arthritis, cancer, and kidney failure were more likely to have depression than those without these illnesses. The greatest odds of depression in the illness category were found among respondents who reported having cancer (OR = 2.63). People with difficulty accessing healthcare were also more likely to have depression (OR = 1.41–1.45). Marginal effects of healthcare access and household expenditure Marginal effect analyses were employed to examine the predicted probability of having depression with healthcare access and household expenditure as outcome variables. Supplementary Fig. 1 shows the marginal effect of healthcare access based on each type of coastal hazard. Controlled for socio-demographic characteristics and types of illness, respondents who had difficulty accessing healthcare were more likely to have depression than those with less difficulty accessing healthcare. Supplementary Fig. 2 describes the marginal effects of household expenditure based on each type of coastal hazard. The results highlight that individuals belonging to the poorest households were more likely to have depression. Discussion This study is among the first to address the effect of sea level rise on depression using nationally representative data from Indonesia. Nearly one-third of the respondents to the survey lived in areas affected by coastal hazards, and the prevalence of depression was higher among them (6.7%) than among those not affected by coastal hazards (5.7%). Living in a coastal hazard area is correlated with 1.13 higher odds of having depression. This finding supports the hypothesis that climate change-induced events can impact mental health, consistent with existing evidence of the mental health impacts from rising sea levels in the Solomon Islands, 15 the United States 29 and the Pacific Islands. 30 Using data from two coastal counties in the US, Monsour and colleagues found that individuals affected by tropical cyclones were at higher risk of major depressive disorders (coefficients between 2.2 and 2.8). 29 A study in the Pacific Islands interviewed 100 Tuvalian participants and revealed that 62% of them experienced psychological distress. 30 Asugeni and colleagues interviewed 57 individuals living in a remote coastal region of the Solomon Islands. They showed that 90% of them stated that they feared and worried about the impact of sea level rise. 15 Sea level rise may affect mental health directly by exposing people to trauma. Rising sea levels cause shoreline abrasion 31 and escalate the risk of coastal flooding. 32 Our findings showed that individuals living in areas with coastal abrasion, hurricanes, and tidal flooding were 1.16, 1.14, and 1.24 times more likely to have depression than those living outside these areas. A prior study using data from the English National Study of Flooding and Health revealed that flooding was associated with 7.77, 4.16 and 14.70 higher odds of depression, anxiety and PTSD, respectively. 33 The risks of depression among people affected by extreme events in our study are lower than the study from the UK, probably due to the lower prevalence of depression in the national survey in Indonesia used in this study (6%) compared to the prevalence of depression in the UK (16%). 34 Depression and anxiety scores among individuals affected by seasonal floods in Northern India have also been found to be higher than those not affected. 35 The effects of hurricanes on mental health have been documented in several studies. 36 37 For example, Kohn assessed PTSD and major depressive disorder in hurricane-affected adults at two months and two years after the event. 36 The study found that the prevalence of both diseases has remained generally steady over time (PTSD 10.6% and 11.8%, major depressive disorder 19.5% and 19.4% at two months and two years). The impact of hurricanes has also been found to be persistent in New York City and Long Island residents. 37 Having depression and anxiety immediately after a hurricane was a predictor of persistent depression and anxiety a year later. Coastal hazards also lead to the physical loss of land and dwellings, food and water shortages, loss of employment, and eventual displacement, including migration. Munro and colleagues found a strong link between displacement following the 2013-14 floods in the UK and the prevalence of depression, anxiety, and PTSD one year later. 38 A link between displacement from one’s home after a climate-related disaster and growing mental health symptoms has also been found in Bangladesh. 39 Coastal hazards can also affect mental health indirectly by affecting physical health and community well-being. Sea level rise is associated with increased exposure to waterborne pathogens, vector-borne diseases, saltwater intrusion, and poor air quality due to mould. 40 For example, the sea level change had a positive and significant association with a higher prevalence of dengue disease in Malaysia. 41 Poor physical health may thus increase the risk of depression among people living in coastal communities. Our findings further identify that women, those with low educational attainment, informal workers, those with lower incomes, those with comorbidities and those with difficulties accessing healthcare were among the groups with a higher risk of depression when exposed to coastal hazards. Women may be at increased risk of depression due to gender-differentiated social roles and a lack of access to resources. A study in coastal Bangladesh looked at Hurricane Aila’s effects in 2009 and found that women could not attend nongovernmental organisation (NGO) training or income-generating activities without their husbands’ approval due to gender roles. The researchers also discovered that labouring in the fields in the rising heat, extracting water from seawater-contaminated wells, and damaged infrastructure by recurring tidal flooding made it more difficult for women to obtain everyday resources, which led to increased levels of hunger. 42 Rising sea levels inundate coastal areas, swallowing up agricultural land in Indonesia. This land loss directly impacts food production, especially rice, a vital crop for Indonesia. Studies estimate millions of hectares of farmland could be lost. 43 A review showed that women suffer more detrimental impacts of climate change because of social and cultural norms regarding gender, such as women tend to have less power in decision-making in the family, as well as a lack of access to and control over assets, with some exceptions. 44 Literature has highlighted that poverty is a major factor influencing people’s vulnerability to climate-related shocks and stressors, 45 and thus may be at higher risk of mental disorders. According to a study in Bangladesh, having an unpaid job or suffering considerable income loss during Cyclone Amphan was significantly associated with higher psychological distress symptoms. 39 Climate-related disasters, including sea level rise, may destroy crops, land, and critical infrastructure. These lead to reduced agricultural output and increased prices of major crops, greatly impacting food production and threatening food security. 46 Lower output of crops means lower incomes for the most vulnerable people. Under these conditions, the poorest people will be those most affected by sea level rise as they already use most of their incomes for food and require additional income to meet their daily nutritional requirements. Several South Asian countries, including Bangladesh, India, Pakistan and Nepal, launched cash transfer programs to help low-income families cope with climate-related disasters and income losses. 47 Indonesia’s government also provides a cash transfer program for poor people called Direct Cash Assistance ( Bantuan Langsung Tunai ) program. However, little is known regarding the effectiveness of this program in helping poor communities cope with climate change. There are some limitations to this study that should be noted. First, our study is cross-sectional, which implies important limitations to the interpretability of data in terms of finding causal effects between variables. Second, the only measure available is depression. More severe mental disorders, including PTSD, acute stress disorders, anxiety, substance use and suicide, may occur as a result of the impact of climate change-related disasters. 3 Our data was taken in 2018 and needs to be updated with the rapid changes in the environment due to climate change. Future studies, including more mental disorders, could better capture the consequences of sea level rise on mental health. Targeted coproduced qualitative works are also required to integrate and refine the mechanisms of action and most modifiable intervention points. In conclusion, living in areas affected by coastal hazards in Indonesia is related to higher odds of having depression, and almost one-third of the Indonesian population (31.3%) live in these areas. It is thus important to implement interventions to mitigate the impact of sea level rise on mental health and well-being. So far, the available adaptation strategies for sea level rise have been focused on preventing land damage, such as building sea walls, reforestation, upgrading existing drainage infrastructure, and preventing socio-economic damage due to the loss of land. 48 49 Our findings highlight the importance of further research on interventions to address the impact of rising sea levels on mental health. Based on our findings, the mental health of women, those with informal jobs, low education and less income, those with comorbidities and those with difficulty accessing healthcare are more likely to be affected by sea level rise. In addition, pre-existing health burdens make people more vulnerable to sea-level effects by reducing resources. Therefore, we need to continue to emphasise investment in mental health and non-communicable disease management to ensure resilience in this impending context. Adaptation strategies and mental health interventions should thus target these groups. Declarations AUTHOR CONTRIBUTIONS A.M.: conceptualisation, methodology, data curation, writing—original draft, writing—review and editing, funding acquisition. S.S.: methodology, data curation, writing—original draft, writing—review and editing, funding acquisition. H.S., H.B., P.B.: writing—review and editing, funding acquisition. FUNDING STATEMENTS This research was funded by the Faculty of Nursing at Universitas Indonesia (Award ID NKB-217/UN2.RST/HKP.05.00/2024)]. The views expressed in this publication are those of the author(s) and not necessarily those of the funder. DATA AVAILABILITY STATEMENT Indonesia Basic Health Survey (Riset Kesehatan Dasar or Riskesdas) data are available through the Health Policy and Development Agency, Ministry of Health, Republic of Indonesia, at https://layanandata.kemkes.go.id/. The form for data requests can be found in https://layanandata.kemkes.go.id/request. COMPETING INTEREST: The authors declare no competing interests. References World Health Organization. Mental health and climate change: Policy brief. 2022. World Meteorological Organization. Atlas of Mortality and Economic Losses from Weather, Climate and Water-related Hazards 2023, [Available from: https://wmo.int/publication-series/atlas-of-mortality-and-economic-losses-from-weather-climate-and-water-related-hazards]. Lawrance, E., Thompson, R., Fontana, G. & Jennings, N. The impact of climate change on mental health and emotional wellbeing: current evidence and implications for policy and practice. Grantham Institute briefing paper 36 , 1-36 (2021). 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Effect of evacuation and displacement on the association between flooding and mental health outcomes: a cross-sectional analysis of UK survey data. The Lancet Planetary Health 1 (4), e134-e141 (2017). Hossain, A., Ahmed, B., Rahman, T., Sammonds, P., Zaman, S., Benzadid, S., & Jakariya, M. Household food insecurity, income loss, and symptoms of psychological distress among adults following the Cyclone Amphan in coastal Bangladesh. Plos One 16 (11), e0259098 (2021). Wade T, R. Health risks associated with sea level rise (2022). Tan, C. H., Lee, S. N., & Ho, S. B. Assessing the environmental effects on dengue fever and Malaysian economic growth. International Journal of Environmental Science and Development 13 (2), 49-56 (2022). Al Nahian, M., Islam, G., & Bala, S. K. A new approach in gender vulnerability assessment using matrix framework. Proceedings of the 4th International Conference on Water & Flood Management (2013). Riadi, B., Barus, B., Widiatmaka, M. Y. J., & Pramudya, B. Spatial Modeling on Coastal Land Use/Land Cover Changes and its Impact on Farmers. Environment and Ecology Research 6 (3), 169-177 (2018). Goh A., H. A literature review of the gender-differentiated impacts of climate change on women’s and men’s assets and well-being in developing countries (2012). Thomas, K., Hardy, R. D., Lazrus, H., Mendez, M., Orlove, B., Rivera‐Collazo, I., ... & Winthrop, R. Explaining differential vulnerability to climate change: A social science review. Wiley Interdisciplinary Reviews: Climate Change 10 (2), e565 (2019). Kumar, P., Tokas, J., Kumar, N., Lal, M., & Singal, H. R. Climate change consequences and its impact on agriculture and food security. International Journal of Chemical Studies 6 (6), 124-133 (2018). Islam, M., S., & Hasan, A., R. (2019). Social safety net program in strengthening adaptive capacity to disaster and climate change in South Asia: problems and prospects. Social Science Review 36 (1): 63-76 (2019). Dedekorkut-Howes, A., Torabi, E., & Howes, M. When the tide gets high: A review of adaptive responses to sea level rise and coastal flooding. Journal of Environmental Planning and Management 63 (12), 2102-2143 (2020). Bongarts Lebbe, T., Rey-Valette, H., Chaumillon, É., Camus, G., Almar, R., Cazenave, A., ... & Euzen, A. Designing coastal adaptation strategies to tackle sea level rise. Frontiers in Marine Science 8 , 740602 (2021). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4442319\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":308471297,\"identity\":\"de4a28ff-5edf-46e9-886a-d74babc62665\",\"order_by\":0,\"name\":\"Asri 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among districts in Indonesia 2016-2018\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FIgure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4442319/v1/7f2b6addeccf00b5fe30aa30.png\"},{\"id\":57954905,\"identity\":\"336ccfae-6890-41e4-b244-b303566e875f\",\"added_by\":\"auto\",\"created_at\":\"2024-06-07 23:24:37\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1446070,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eGeographical distribution of depression prevalence in (%) among districts in Indonesia 2018\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4442319/v1/6df9932d0b841f45902af6dd.png\"},{\"id\":77622503,\"identity\":\"738a8396-7be9-4397-b363-74434fea166c\",\"added_by\":\"auto\",\"created_at\":\"2025-03-03 16:07:38\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":4319184,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4442319/v1/14e82f8d-6f62-4a8d-9720-1fd53ba44560.pdf\"},{\"id\":57954607,\"identity\":\"1adc3202-b225-43c9-9e7e-636457d45134\",\"added_by\":\"auto\",\"created_at\":\"2024-06-07 23:16:37\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":276816,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTablesandFigures.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4442319/v1/dd7928a93309c9cb317f21ff.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Depression among people who live in coastal hazard areas in Indonesia: Evidence from a population-based national survey\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eClimate change is widely regarded as a contributing factor to a growing number of worldwide emergencies, which have a significant effect on the impacted population\\u0026rsquo;s mental health and well-being.\\u003csup\\u003e1\\u003c/sup\\u003e Extreme weather due to climate change has caused 2.1\\u0026nbsp;million deaths and globally cost \\u003cspan\\u003e$\\u003c/span\\u003e4.3 trillion in economic losses in the past five decades.\\u003csup\\u003e2\\u003c/sup\\u003e Evidence of the consequences of climate change on mental health and well-being has been accumulating.\\u003csup\\u003e3 4\\u003c/sup\\u003e Prior studies have shown that exposure to climate-related stressors, such as heat, humidity, rainfall, drought, wildfires, and floods, have been linked to increased psychiatric hospitalisations,\\u003csup\\u003e5\\u003c/sup\\u003e suicide rates,\\u003csup\\u003e6\\u003c/sup\\u003e and psychological distress,\\u003csup\\u003e7\\u003c/sup\\u003e in addition to worsening mental health and higher mortality among individuals with pre-existing mental health conditions.\\u003csup\\u003e8 9\\u003c/sup\\u003e Without action, these burdens will become greater over the coming decades.\\u003c/p\\u003e \\u003cp\\u003eAmong the most significant consequences of climate change is rising sea levels. The global mean sea level has increased by about 20 cm since 1900.\\u003csup\\u003e10\\u003c/sup\\u003e The rise of sea level has been accelerating during the 20th and early 21st centuries, and it has increased at a rate of roughly 3.6 mm every year from 2006 to 2015.\\u003csup\\u003e11\\u003c/sup\\u003e Over 600\\u0026nbsp;million people worldwide reside in low-lying coastal regions at a height of less than 10 metres. By 2050, the number of people living in these areas is anticipated to surpass 1\\u0026nbsp;billion.\\u003csup\\u003e12\\u003c/sup\\u003e Sea level rise is predicted to exacerbate coastal erosion, catastrophic marine flooding, and saltwater intrusion in coastal aquifers.\\u003csup\\u003e13\\u003c/sup\\u003e It has further heightened the impact of hurricanes in coastal areas.\\u003csup\\u003e14\\u003c/sup\\u003e Sea level rise increases the risk of many forms of bodily harm: injury or drowning when coupled with extreme weather, infectious diseases from increased exposure to waterborne or vector-borne pathogens, health effects from increased exposure to contaminants or airborne pollutants, and numerous negative effects on social determinants of health. It is thus also important to understand the current and future mental health consequences of sea level rise in those communities most vulnerable to it. In this study, we examine areas affected by sea level rise to enable the development of targeted resilience building and intersectoral mitigation strategies.\\u003c/p\\u003e \\u003cp\\u003eRecent studies have begun responding to the challenges presented by sea level rise in affected locations. In a study in the Solomon Islands, almost all (56 out of 57) respondents stated that they and their families were being impacted by sea level rise, which was creating anxiety and panic both personally and throughout the community.\\u003csup\\u003e15\\u003c/sup\\u003e Kelman et al. (2020) reported that populations in small island developing states experienced major adverse effects on mental health and well-being linked to climate change, including acute stress, anxiety, depression, and post-traumatic stress disorder (PTSD).\\u003csup\\u003e16\\u003c/sup\\u003e A study in two counties in Florida, USA, revealed that sea level rise and tropical cyclones were associated with a high risk of PTSD, anxiety, and major depressive disorder. However, these studies focused on limited geographical areas or selected islands and atolls and used community or volunteer samples rather than national ones. Hence, their findings cannot be widely generalised.\\u003c/p\\u003e \\u003cp\\u003eThis study addresses the above-mentioned gap by combining two nationally representative datasets from Indonesia. As the world\\u0026rsquo;s largest archipelagic country, Indonesia\\u0026rsquo;s coastal area is vulnerable to climate change. Indonesia ranks fifth in the world in terms of residents living in lower-elevation coastal zones, and without adaptation, the total population at risk of permanent flooding by 2070\\u0026ndash;2100 could exceed 4.2\\u0026nbsp;million people.\\u003csup\\u003e17\\u003c/sup\\u003e Climate change has altered the characteristics of Indonesia\\u0026rsquo;s coastline, including a decline in the natural coastline and an increase in the artificial coastline.\\u003csup\\u003e18\\u003c/sup\\u003e Over the last fifteen years, Indonesia has lost 29,261 hectares of its coastline\\u0026mdash;an area about the size of Jakarta\\u0026mdash;while natural sedimentation creates 895 hectares of new coastal land annually. According to a Ministry of Maritime Affairs and Fisheries report, erosion is causing Indonesia to lose some 1,950 hectares of coastal land annually.\\u003csup\\u003e19\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eFurthermore, floods, the possibility of irreversible flooding, and the incursion of saltwater into freshwater resources could render other locations uninhabitable.\\u003csup\\u003e20\\u003c/sup\\u003e Prior studies have revealed the increasing health risks, including vector-borne diseases, tuberculosis, diarrhoea, and skin diseases, due to climate change among coastal populations in two areas in Indonesia: Semarang\\u003csup\\u003e21\\u003c/sup\\u003e and Manado.\\u003csup\\u003e22\\u003c/sup\\u003e Certain groups are more vulnerable to the health effects of climate change than others because of social and economic factors such as income, education, access to health care, and housing. Identifying these groups is crucial in designing interventions to tackle the burden of mental disorders in areas affected by climate change. However, there is no evidence of the mental health burden in coastal areas affected by the rise in sea level in Indonesia. This study thus aims to:\\u003c/p\\u003e \\u003cp\\u003e1. Examine the consequences of living in areas affected by coastal hazards on the risk of depression.\\u003c/p\\u003e \\u003cp\\u003e2. Evaluate the effects of coastal hazards, i.e., abrasions, hurricanes, and tidal flooding, on depression.\\u003c/p\\u003e \\u003cp\\u003e3. Identify groups within the population living in areas with coastal hazards at higher risk of depression.\\u003c/p\\u003e\"},{\"header\":\"Study design and method\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy design\\u003c/h2\\u003e \\u003cp\\u003eThis study used a cross-sectional design with data from the most recent Indonesia Basic Health Survey (\\u003cem\\u003eRiset Kesehatan Dasar\\u003c/em\\u003e or \\u003cem\\u003eRiskesdas\\u003c/em\\u003e) 2018.\\u003csup\\u003e23\\u003c/sup\\u003e Riskesdas is a nationally representative survey conducted every five years in all 34 provinces and 514 districts of Indonesia by the National Institute of Health Research and Development (NIHRD), Ministry of Health; it focuses on the measurement of health indicators mandated by the Millennium Development Goals or Sustainable Development Goals, including mental health and non-communicable diseases.\\u003c/p\\u003e \\u003cp\\u003e The Ethics Committee of the NIHRD provided ethical clearance before data collection. Because we conducted secondary data analyses, the pre-requisite of ethics approval is not applicable. Prior to the data collection and before the interviews were conducted, the enumerators obtained informed consent from the respondents in the form of written consent.\\u003c/p\\u003e \\u003cp\\u003eParticipants were selected using a multistage systematic random sampling method. The first stage identified groups of census blocks and designated them as primary sampling units (PSUs). The second stage used a probability proportional sampling method to design the enrollment size to identify a census block from each PSU. The third comprised systematic random sampling of 25 census buildings from each block. One household from each census building was randomly chosen in the fourth stage. All household members (defined as those having stayed on the premises for the past six months or more and having the same financial source for food) of each selected household were asked to participate in the survey. The study sample focuses on adults aged 18 years and older who had completed information on mental health depression questions. The total number of respondents whose information was used in the analyses was 642,419.\\u003c/p\\u003e \\u003cp\\u003eThe Riskesdas 2018 data was linked to the Village Survey (\\u003cem\\u003ePotensi Desa\\u003c/em\\u003e or Podes) data in the same year (2018). Podes provides information on potential assets that belong to the smallest area unit in Indonesia, i.e. the village, including its social economy, infrastructure, and human resources. For this study, we retrieved information on whether the village is situated in a coastal area and has experienced abrasion, hurricanes, and/or tidal flooding in the past three years. We aggregated the data at the district level and supplemented them with individual data from Riskesdas 2018 using district codes from Indonesia Statistics. Taken together, these data capture the nested structure of individuals within districts.\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eEthical approval\\u003c/strong\\u003e \\u003cp\\u003e for Riskesdas was obtained via the Ethical Commission of the National Institute of Health Research and Development, Ministry of Health, the Republic of Indonesia (No. LB.02.01/2/KE.267/2017). Participants gave informed consent to participate in the study before taking part. Procedures were performed in accordance with national guidelines and regulations for research activities.\\u003c/p\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMeasures of depression\\u003c/h2\\u003e \\u003cp\\u003eDepression was assessed using the Mini-International Neuropsychiatric Interview (MINI) version 6 of the Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV and the International Classification of Diseases \\u0026minus;\\u0026thinsp;10, a structured diagnostic psychiatric interview used to assess various mental health problems.\\u003csup\\u003e24\\u0026ndash;26\\u003c/sup\\u003e It is widely used in clinical and research settings and has been translated into various languages. A previous study validated the Indonesian version of the MINI depression section.\\u003csup\\u003e27\\u003c/sup\\u003e The questionnaire consists of ten questions with \\u0026ldquo;yes\\u0026rdquo; or \\u0026ldquo;no\\u0026rdquo; answers. A respondent was classed as depressed if they answered \\u0026ldquo;yes\\u0026rdquo; to at least two of the questions numbered 1 to 3 and \\u0026ldquo;yes\\u0026rdquo; to at least two of the questions numbered 4 to 10.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMeasures of coastal hazards\\u003c/h2\\u003e \\u003cp\\u003eWe categorised the district as a coastal area if it has villages immediately on the coastline. We classified the respondents as living in an area with coastal abrasion, living on a coastline with hurricanes, and having regular experience with tidal flooding if they lived in a district having experienced those disasters in the past three years.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCovariates\\u003c/h2\\u003e \\u003cp\\u003eWe classified individual-level risk factors of depression as demographic, socio-economic, health behaviour-related, health status-related, and household characteristic-related. The demographic factors included age group (18\\u0026ndash;24 as reference, 25\\u0026ndash;34, 35\\u0026ndash;44, 45\\u0026ndash;54, 55\\u0026ndash;64, 65\\u0026ndash;74, 75+) and sex (male as reference). Marital status was categorised as single (reference), married, or divorced/widowed. The socio-economic factors included the highest attained educational level (primary school or less, junior high school, and senior high school and higher as reference), household expenditure per month in quintiles, and occupation (unemployed as reference, student, employed/retired, self-employed, informal worker, and other).\\u003c/p\\u003e \\u003cp\\u003eSmoking, alcohol use and physical activity were included as indicators of health behaviour determinants. We categorised smoking behaviour into smoking every day (reference), not every day, past smoker and non-smoker. Similarly, alcohol use was classified as drinking alcohol under standard (reference), more than standard, or never drinking alcohol. The presence of chronic diseases was measured using self-reports of ever having been diagnosed by a health professional as suffering from tuberculosis, hypertension, stroke, diabetes mellitus, heart disease, asthma, rheumatoid arthritis, cancer, or renal failure. Principal component analysis (PCA) was used to determine the degree of difficulty in accessing healthcare services based on three dimensions of information: (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) the modes of transportation used to access healthcare services; (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e) the round-trip time from the home to the healthcare provider; and (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e) the round-trip transportation cost to the healthcare provider.\\u003csup\\u003e28\\u003c/sup\\u003e We divided the challenges associated with reaching healthcare providers into two categories: easy (score\\u0026thinsp;\\u0026ge;\\u0026thinsp;mean/median) and difficult (score\\u0026thinsp;\\u0026lt;\\u0026thinsp;mean/median).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analyses\\u003c/h2\\u003e \\u003cp\\u003eWe analysed the data in several steps. Firstly, we created maps to illustrate the importance of residential areas with regard to coastal hazards and the prevalence of depression across 514 districts in Indonesia. Secondly, we described the characteristics of the respondents in terms of residential area and the presence of depression. We presented the odds ratios (ORs) and significance of the difference by implementing the alpha of 5% error through χ2 analysis for the covariates with two categories and binary logistic regression for the covariates with more than two categories. Finally, multivariable logistic regression analysis was conducted to examine the relationship between living in a coastal hazard area and the risk of depression. We performed the multivariable logistic regression for all samples and respondents living in areas with all three types of coastal hazards combined and separately for those living in areas with abrasion, hurricanes, and tidal floods. We calculated marginal effects to estimate the predicted probability of having depression with healthcare access and household expenditure as outcome variables. We used STATA 18.0 for this analysis, considering the corresponding Riskesdas weights, strata, and primary sampling unit according to its survey design.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec9\\\"\\u003e\\n \\u003ch2\\u003eGeographical distribution of SLR and depression\\u003c/h2\\u003e\\n \\u003cp\\u003eFigure \\u003cspan\\u003e1\\u003c/span\\u003e describes the geographical distribution of abrasion, hurricanes, and tidal flooding across districts in Indonesia. The map shows the variations in occurrence of these natural hazards, with the highest number found across the northern and southern Java coastlines, northeastern and southern Sumatra coastlines, West Sumatra coastline, northwest Sulawesi coastline, southern Papua coastline, and on the small islands of southern Maluku and eastern Nusa Tenggara. More than one hundred of the coastline hazard areas are found on those islands.\\u003c/p\\u003e\\n \\u003cp\\u003eThe geographical variation in the prevalence of depression across districts is also apparent (Fig. \\u003cspan\\u003e2\\u003c/span\\u003e). People living across the northern and southern Java coastlines, northeastern Sumatra, West Sumatra and the southeastern Sumatra coastline, northwestern Sulawesi coastline, southern Papua coastline, and on the small islands of southern Maluku and eastern Nusa Tenggara had the highest prevalence of depression with 20\\u0026ndash;30% of their populations having depression.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec10\\\"\\u003e\\n \\u003ch2\\u003eCharacteristics of respondents\\u003c/h2\\u003e\\n \\u003cp\\u003eThe first four columns of \\u003cstrong\\u003eTable\\u0026nbsp;1\\u003c/strong\\u003e describe the characteristics of respondents\\u0026rsquo; socio-demographic and depression status based on living area (coastal or non-coastal). A detailed description of respondents based on types of coastal hazards (abrasion, hurricane, and tidal flood exposure) is presented in \\u003cstrong\\u003eSupplementary Tables S1\\u003c/strong\\u003e and \\u003cstrong\\u003eS2\\u003c/strong\\u003e.\\u003c/p\\u003e\\n \\u003cp\\u003eThe percentage of respondents who lived in coastal hazard areas was 31.3%. The prevalence of depression among respondents living in coastal hazard areas (6.7%) was higher than among those living outside coastal hazard areas (5.7%). The highest prevalence of depression was found among people living with tidal flood exposure (7.5%). The age distributions in the two types of areas were relatively similar, with 23.5% being in the 35\\u0026ndash;44 age group. In both types of areas, the proportion of females was slightly higher than males. The percentage of respondents educated at the primary school level or less was higher among those living in coastal hazard areas (48.8%) than those living outside (43.5%). The highest proportion of people with a low educational level was found among those living in tidal flood areas (51.4%). Likewise, the percentage of respondents working in informal sectors within coastal hazard areas was higher (37.4%) than those living outside (33.8%). The highest percentage of informal workers was found among people living in tidal flood areas (40.0%). The proportion of families within the first quintile of expenditures within coastal hazard areas was also larger (20.9%) than those outside (16.4%). The highest percentage of low-income families was located in coastal areas frequently facing tidal floods (23.0%). People living in coastal hazard areas had less access to healthcare than their counterparts, as 14.1% of respondents living in those areas reported difficulty accessing healthcare compared with 10.1% of those living outside them. The highest percentage of respondents facing difficulty accessing healthcare appeared in coastal areas frequently facing tidal floods (15.8%).\\u003c/p\\u003e\\n \\u003cp\\u003eSmoking was slightly more prevalent among people living in coastal hazard areas (27.2%) than 26.8% of those living outside coastal hazards. The highest percentage of smokers was found among people living in tidal flood areas (28.5%). Likewise, the proportions of those using alcohol both under and above standard were larger among people living in coastal hazard areas (3.3% and 3.2%, respectively) than those living outside coastal hazard areas (2.5% and 1.4%, respectively). People living in coastline areas were also less likely to engage in physical activity (10.1%) than those living outside coastline areas (9.8%). The highest percentage of people with less physical activity was found among people living in tidal flood areas (9.4%). The most common illnesses among people living in coastal hazard areas were hypertension (8.7%) and rheumatoid arthritis (8.0%), while the least common was cancer (0.3%).\\u003c/p\\u003e\\n \\u003cp\\u003eThe last three columns of \\u003cstrong\\u003eTable\\u0026nbsp;1\\u003c/strong\\u003e show the characteristics of respondents living in areas experiencing coastal hazards with respect to the presence of depression. We found the highest percentage of depression among respondents living in coastal hazard areas within the 35\\u0026ndash;44 age group (22.1%), females (62.7%), those with primary school or less education (69.7%), informal workers (37.4%) and unemployed people (37.0%), those in the first quintile of expenditures (25.5%), and those with difficulty to access healthcare (20.8%). These socio-demographic characteristics were similar among people living in areas where abrasion, hurricanes, and tidal flooding often occur, with a slightly higher percentage found among people living in tidal flooding areas.\\u003c/p\\u003e\\n \\u003cp\\u003eAbout one-fifth of respondents living in coastal hazard areas with depression smoked every day (23.4%). The highest percentage of smokers was found among depressed people living in tidal flooding areas (24.6%). The percentage of alcohol use was higher among respondents living in coastal hazard areas with depression than those without depression. One-tenth of these reported having little physical activity. Among depressed people, 14.7% had rheumatoid arthritis, and 14.2% had hypertension.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec11\\\"\\u003e\\n \\u003ch2\\u003eMultivariable logistic regression\\u003c/h2\\u003e\\n \\u003cp\\u003eTable \\u003cspan\\u003e2\\u003c/span\\u003e describes the results of the multivariable logistic regression. Respondents living in coastal hazard areas were 1.13 more likely to have depression than those living outside them. Likewise, respondents living in areas with coastal abrasions were 1.16 times more likely to have depression than those living outside those areas. Those living in coastal areas with hurricanes were 1.14 times more likely to have depression than their counterparts. Those living in coastal areas with tidal flooding were 1.24 times more likely to have depression. In all models, young adults (18\\u0026ndash;24 years) were more likely to experience depression than members of other age groups. Females were 2.16\\u0026ndash;2.17 times more likely to experience depression than males. Those who graduated from junior high school and those with a primary school education or less were more likely to have depression than those who graduated from senior high school. Married people were less likely to have depression than single people. The odds of being depressed for divorced and widowed individuals were 1.12\\u0026ndash;1.13 times greater than those of single individuals. In all models, jobless individuals were more likely to have depression than employed individuals.\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTabel 1\\u003c/strong\\u003e Characteristics of all respondents based on residential area (with or without coastal hazards) and of respondents living in coastal hazard areas by presence of depression\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"7\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eVariables\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eAll respondents\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eRespondents living in coastal hazard areas\\u003c/p\\u003e\\n \\u003cp\\u003e(N\\u0026thinsp;=\\u0026thinsp;201,109)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNon-coastal hazard areas\\u003c/p\\u003e\\n \\u003cp\\u003e(N\\u0026thinsp;=\\u0026thinsp;441,310)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCoastal hazard areas\\u003c/p\\u003e\\n \\u003cp\\u003e(N\\u0026thinsp;=\\u0026thinsp;201,109)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ep-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNot depressed\\u003c/p\\u003e\\n \\u003cp\\u003e(N\\u0026thinsp;=\\u0026thinsp;187,548)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDepressed\\u003c/p\\u003e\\n \\u003cp\\u003e(N\\u0026thinsp;=\\u0026thinsp;13,561)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ep-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDepression\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e416,256 (94.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e187,548 (93.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e25,054 (5.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13,561 (6.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAge\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e18\\u0026ndash;24 years old\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e59,746 (13.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e26,962 (13.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e25,133 (13.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1,829 (13.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e25\\u0026ndash;34 years old\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e90,473 (20.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e41,953 (20.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e39,616 (21.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2,337 (17.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e35\\u0026ndash;44 years old\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e104,336 (23.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e47,358 (23.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e44,361 (23.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2,997 (22.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e45\\u0026ndash;54 years old\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e88,343 (20.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e39,594 (19.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e36,818 (19.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2,776 (20.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e55\\u0026ndash;64 years old\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e58,999 (13.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e26,188 (13.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e24,269 (12.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1,919 (14.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e65\\u0026ndash;74 years old\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e26,677 (6.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e12,909 (6.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e11,808 (6.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1,101 (8.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e75 years old and above\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e12,736 (2.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6,145 (3.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5,543 (3.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e602 (4.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGender\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e210,023 (47.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e94,678 (47.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e89,618 (47.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5,060 (37.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e231,287 (52.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e106,431 (52.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e97,930 (52.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8,501 (62.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEducation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSenior high school or higher\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e169,555 (38.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e70,420 (35.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e67,044 (35.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3,376 (24.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eJunior high school\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e79,851 (18.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e32,617 (16.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e30,505 (16.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2,112 (15.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePrimary school or lower\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e191,904 (43.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e98,072 (48.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e89,999 (48.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8,073 (59.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMarital status\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUnmarried\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e68,421 (15.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e30,423 (15.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e28,417 (15.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2,006 (14.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMarried\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e329,056 (74.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e151,013 (75.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e141,564 (75.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e9,449 (69.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDivorced or widowed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e43,833 (9.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e19,673 (9.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e17,567 (9.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2,106 (15.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEmployment status\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eJobless\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e128,695 (29.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e57,798 (28.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e52,780 (28.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5,018 (37.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStudent\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e12,936 (2.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5,840 (2.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5,404 (2.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e436 (3.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEmployed or retired\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e56,378 (12.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e21,946 (10.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e21,165 (11.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e781 (5.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSelf-employed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e66,687 (15.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e24,635 (12.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e23,398 (12.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1,237 (9.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eInformal worker\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e149,199 (33.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e75,227 (37.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e70,158 (37.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5,069 (37.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e27,415 (6.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e15,663 (7.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e14,643 (7.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1,020 (7.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMonthly household expenditure by quintile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1st quintile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e72,460 (16.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e41,935 (20.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e38,480 (20.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3,455 (25.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2nd quintile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e80,993 (18.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e40,228 (20.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e37,243 (19.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2,985 (22.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3rd quintile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e87,415 (19.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e39,936 (19.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e37,237 (19.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2,699 (19.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4th quintile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e94,449 (21.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e40,326 (20.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e37,866 (20.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2,460 (18.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5th quintile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e105,993 (24.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e38,684 (19.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e36,722 (19.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1,962 (14.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHave difficulty accessing healthcare\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e361,986 (89.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e150,424 (85.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e141,514 (86.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8,910 (79.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e40,706 (10.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e24,654 (14.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e22,320 (13.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2,334 (20.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSmoking status\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEvery day\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e118,580 (26.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e54,712 (27.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e51,538 (27.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3,174 (23.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNot every day\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e20,187 (4.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e9,706 (4.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e9,031 (4.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e675 (5.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFormer smoker\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e24,107 (5.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e9,630 (4.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8,831 (4.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e799 (5.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNever smoked\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e278,436 (63.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e127,061 (63.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e118,148 (63.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8,913 (65.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAlcohol use\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUnder standard\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e11,145 (2.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6,685 (3.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6,121 (3.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e564 (4.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMore than standard\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6,261 (1.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6,420 (3.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5,873 (3.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e547 (4.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo alcohol\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e423,904 (96.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e188,004 (93.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e175,554 (93.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e12,450 (91.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePhysical activity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLess active\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e43,399 (9.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e20,216 (10.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.007\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e18,743 (10.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1,473 (10.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eActive\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e397,911 (90.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e180,893 (89.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e168,805 (90.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e12,088 (89.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHave you ever been diagnosed with lung tuberculosis?\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e439,019 (99.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e200,118 (99.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.169\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e186,715 (99.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13,403 (98.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2,291 (0.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e991 (0.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e833 (0.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e158 (1.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHave you ever been diagnosed with hypertension?\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e401,688 (91.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e183,702 (91.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e172,063 (91.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e11,639 (85.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e39,622 (9.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e17,407 (8.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e15,485 (8.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1,922 (14.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHave you ever been diagnosed with stroke?\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e435,842 (98.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e198,781 (98.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.006\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e185,669 (99.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13,112 (96.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5,468 (1.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2,328 (1.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1,879 (1.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e449 (3.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHave you ever been diagnosed with diabetes mellitus?\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e430,876 (97.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e197,059 (98.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e184,027 (98.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13,032 (96.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e10,434 (2.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4,050 (2.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3,521 (1.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e529 (3.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHave you ever been diagnosed with heart disease?\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e432,384 (98.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e197,557 (98.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e184,484 (98.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13,073 (96.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8,926 (2.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3,552 (1.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3,064 (1.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e488 (3.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHave you ever been diagnosed with asthma?\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e429,663 (97.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e195,512 (97.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e182,765 (97.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e12,747 (94.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e11,647 (2.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5,597 (2.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4,783 (2.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e814 (6.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHave you ever been diagnosed with rheumatoid arthritis?\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e402,771 (91.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e184,910 (91.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e173,339 (92.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e11,571 (85.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e38,539 (8.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e16,199 (8.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e14,209 (7.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1,990 (14.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHave you ever been diagnosed with cancer?\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e440,088 (99.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e200,586 (99.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.229\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e187,113 (99.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13,473 (99.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1,222 (0.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e523 (0.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e435 (0.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e88 (0.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHave you ever been diagnosed with kidney failure?\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e439,435 (99.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e200,292 (99.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.284\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e186,860 (99.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13,432 (99.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1,875 (0.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e817 (0.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e688 (0.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e129 (1.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFamily member with psychosis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e437,596 (99.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e199,287 (99.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.010\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e185,991 (99.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13,296 (98.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3,714 (0.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1,822 (0.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1,557 (0.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e265 (2.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003eThe association of household expenditures with the risk of depression varied across respondent categories. In Models 1 and 2, the 2nd quintile of expenditure was not significantly associated with depression. In these models, households in the 3rd, 4th and 5th quintiles were less likely to have depression than those in the 1st quintile of expenditures. In Models 3 and 4, being in the 2nd and 3rd quintiles of household expenditure was not significantly associated with depression. In these models, households within the 4th and 5th quintiles were less likely to have depression than those within the 1st quintile.\\u003c/p\\u003e\\n \\u003cp\\u003eIn all models, past smokers were more likely to have depression than regular smokers (OR\\u0026thinsp;=\\u0026thinsp;1.24\\u0026ndash;1.25). In contrast, non-smokers were less likely to have depression than regular smokers (OR\\u0026thinsp;=\\u0026thinsp;0.62). Those who reported not drinking alcohol were less likely to have depression than those who did consume alcohol. Respondents who engaged in physical activity were less likely to have depression than those who were less active. In all models, individuals with non-communicable diseases (lung tuberculosis, hypertension, stroke, diabetes mellitus, heart disease, asthma, rheumatoid arthritis, cancer, and kidney failure) were more likely to have depression than those without these illnesses. People with difficulty accessing healthcare were more likely to have depression (OR\\u0026thinsp;=\\u0026thinsp;1.37).\\u003c/p\\u003e\\n \\u003cp\\u003eTable \\u003cspan\\u003e3\\u003c/span\\u003e describes the results of multivariable logistic regression for respondents living in coastal hazard areas in general and those living in coastal abrasion areas, coastal hurricane areas, and tidal flooding areas. No significant association with depression was found in the 35\\u0026ndash;44 age group, but other age groups showed a significant association with depression. The results highlight that young adults (18\\u0026ndash;24 years) were more likely to suffer from depression. In all categories of respondents living in coastal hazard areas, females were more likely to have depression than males. Those educated at the junior secondary level and those with a primary school education or less were more likely to have depression than those educated at the senior high school level or higher. Divorced and widowed respondents were 1.10\\u0026ndash;1.16 times more likely to have depression than unmarried respondents. Married respondents were less likely to have depression. In all categories of respondents living in coastal hazard areas, jobless respondents were more likely to have depression than employed and retired respondents.\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003e\\u003cstrong\\u003eTable 2\\u003c/strong\\u003e Multivariable logistic regression results showing the associations between living in areas with coastal hazards and depression\\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eModel 1\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eModel 2\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eModel 3\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eModel 4\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLiving in area with coastal hazards\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.13 [1.10, 1.16]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLiving in area with coastal abrasion\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.16 [1.13, 1.19]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLiving in coastal area with hurricanes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.14 [1.11, 1.16]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLiving in area with tidal flooding\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.24 [1.20, 1.28]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAge group (Ref.: 18\\u0026ndash;24 years old )\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e25\\u0026ndash;34 years old\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.88 [0.83, 0.92]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.88 [0.84, 0.92]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.88 [0.84, 0.92]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.88 [0.84;0.92]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e35\\u0026ndash;44 years old\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.92 [0.87, 0.96]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.92 [0.87, 0.96]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.92 [0.87, 0.96]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.92 [0.87;0.96]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e45\\u0026ndash;54 years old\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90 [0.86, 0.95]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90 [0.86, 0.95]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90 [0.86, 0.95]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90 [0.86;0.95]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e55\\u0026ndash;64 years old\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.74 [0.70, 0.78]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.74 [0.70, 0.78]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.74 [0.70, 0.78]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.74 [0.70;0.78]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e65\\u0026ndash;74 years old\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.71 [0.67, 0.76]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.72 [0.67, 0.76]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.71 [0.67, 0.76]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.72 [0.67;0.76]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e75 years old or above\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.68 [0.63, 0.73]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.68 [0.63, 0.74]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.68 [0.63, 0.73]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.68 [0.63;0.74]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.17 [2.08, 2.25]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.16 [2.08, 2.25]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.16 [2.08, 2.25]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.16 [2.08;2.25]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEducation (Ref.: senior high school or higher)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eJunior high school\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.19 [1.15, 1.23]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.19 [1.15, 1.23]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.19 [1.15, 1.23]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.19 [1.15;1.23]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePrimary school or lower\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.38 [1.34, 1.43]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.38 [1.34, 1.43]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.38 [1.34, 1.43]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.38 [1.34;1.43]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMarital status (Ref.: unmarried)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMarried\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.78 [0.75, 0.81]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.78 [0.75, 0.81]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.78 [0.75, 0.81]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.78 [0.75;0.81]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDivorced or widowed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.13 [1.07, 1.19]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.13 [1.06, 1.19]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.13 [1.07, 1.19]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.12 [1.06;1.19]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEmployment status (Ref.: jobless)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStudent\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.99 [0.92, 1.07]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.00 [0.93, 1.07]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.99 [0.92, 1.07]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.99 [0.92;1.07]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEmployed or retired\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.66 [0.63, 0.70]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.67 [0.64, 0.70]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.66 [0.63, 0.70]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.67 [0.63;0.70]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSelf-employed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.82 [0.78, 0.85]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.82 [0.78, 0.85]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.82 [0.78, 0.85]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.82 [0.78;0.85]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eInformal worker\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.84 [0.82, 0.87]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.84 [0.82, 0.87]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.84 [0.82, 0.87]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.84 [0.82;0.87]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.87 [0.83, 0.92]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.87 [0.83, 0.91]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.87 [0.83, 0.92]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.87 [0.83;0.92]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMonthly household expenditure (Ref.: 1st quintile)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2nd quintile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.98 [0.95, 1.02]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.98 [0.95, 1.02]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.98 [0.95, 1.02]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.98 [0.95;1.02]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3rd quintile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.96 [0.93, 1.00]*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.96 [0.93, 1.00]*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.97 [0.93, 1.00]*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.97 [0.93, 1.00]*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4th quintile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.89 [0.86, 0.92]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.89 [0.86, 0.92]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.89 [0.86, 0.92]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.89 [0.86, 0.92]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5th quintile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.78 [0.75, 0.81]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.79 [0.76, 0.82]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.79 [0.76, 0.82]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.79 [0.76, 0.82]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSmoking status (Ref.: every day)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNot every day\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.00 [0.94, 1.06]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.00 [0.94, 1.06]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.00 [0.94, 1.06]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.00 [0.94, 1.06]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFormer smoker\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.24 [1.18, 1.31]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.25 [1.18, 1.31]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.24 [1.18, 1.31]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.25 [1.19, 1.32]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNever smoked\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.62 [0.60, 0.65]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.62 [0.60, 0.65]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.62 [0.60, 0.65]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.62 [0.60, 0.65]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAlcohol use (Ref.: under standard)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMore than standard\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.97 [0.89, 1.06]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.97 [0.89, 1.06]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.97 [0.89, 1.06]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.97 [0.89, 1.06]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo alcohol\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.55 [0.52, 0.59]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.55 [0.52, 0.58]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.55 [0.52, 0.59]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.55 [0.51, 0.58]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePhysically active\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90 [0.87, 0.94]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.91 [0.87, 0.94]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90 [0.87, 0.94]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90 [0.87, 0.94]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with lung tuberculosis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.20 [1.97, 2.45]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.20 [1.97, 2.45]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.20 [1.97, 2.45]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.20 [1.97, 2.45]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with hypertension\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.42 [1.37, 1.47]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.42 [1.37, 1.47]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.42 [1.37, 1.47]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.42 [1.37, 1.47]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with stroke\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.45 [2.29, 2.62]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.45 [2.29, 2.62]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.45 [2.29, 2.62]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.45 [2.29, 2.62]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with diabetes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.64 [1.54, 1.73]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.64 [1.54, 1.73]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.64 [1.54, 1.73]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.64 [1.54, 1.73]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with heart disease\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.57 [1.48, 1.67]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.57 [1.48, 1.67]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.57 [1.48, 1.67]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.58 [1.48, 1.67]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with asthma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.02 [1.92, 2.13]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.02 [1.92, 2.13]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.02 [1.92, 2.13]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.03 [1.92, 2.13]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with rheumatoid arthritis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.85 [1.79, 1.91]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.85 [1.79, 1.91]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.85 [1.79, 1.91]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.85 [1.79, 1.91]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with cancer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.33 [2.02, 2.69]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.33 [2.02, 2.69]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.34 [2.02, 2.69]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.34 [2.02, 2.69]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with kidney failure\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.08 [1.85, 2.34]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.08 [1.85, 2.34]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.08 [1.85, 2.34]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.08 [1.85, 2.34]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFamily member with psychosis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.33 [2.14, 2.53]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.33 [2.14, 2.53]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.33 [2.14, 2.54]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.34 [2.14, 2.54]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHave difficulty accessing healthcare\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.37 [1.33, 1.42]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.37 [1.33, 1.42]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.37 [1.33, 1.41]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.37 [1.33, 1.41]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIntercept\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.11 [0.10, 0.12]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.11 [0.10, 0.12]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.11 [0.10, 0.12]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.11 [0.10, 0.12]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNumber of observations\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e577770\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e577770\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e577770\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e577770\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e** p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.01, * p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cdiv align=\\\"left\\\"\\u003e\\u003cstrong\\u003eTable 3\\u003c/strong\\u003e Multivariable logistics regression results showing factors associated with depression in areas with (1) coastal hazards, (2) coastal abrasions, (3) hurricanes, and (4) tidal flooding.\\u003c/div\\u003e\\n \\u003ctable id=\\\"Taba\\\" border=\\\"1\\\"\\u003e\\n \\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIndividuals living in areas with coastal hazards\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIndividuals living in areas with coastal abrasion\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIndividuals living in coastal areas with hurricane\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIndividuals living in areas with coastal areas with tidal flooding\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAge group (Ref.: 18\\u0026ndash;24)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e25\\u0026ndash;34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.88 [0.81;0.95]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.86 [0.79;0.94]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.87 [0.80;0.95]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.91 [0.82;1.02]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e35\\u0026ndash;44\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.94 [0.87;1.03]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.93 [0.85;1.02]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.94 [0.86;1.03]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.98 [0.87;1.10]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e45\\u0026ndash;54\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.95 [0.87;1.04]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.95 [0.87;1.05]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.95 [0.87;1.04]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.00 [0.89;1.13]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e55\\u0026ndash;64\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.76 [0.69;0.84]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.76 [0.69;0.85]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.77 [0.69;0.85]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.81 [0.71;0.93]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e65\\u0026ndash;74\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.73 [0.65;0.82]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.73 [0.65;0.83]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.74 [0.66;0.83]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.83 [0.72;0.97]*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e75+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.72 [0.63;0.82]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.73 [0.63;0.85]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.72 [0.62;0.83]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.81 [0.67;0.97]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.03 [1.90;2.18]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.96 [1.81;2.11]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.05 [1.90;2.20]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.97 [1.79;2.16]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEducation (Ref.: senior high school or higher)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eJunior high school\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.20 [1.12;1.28]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.22 [1.14;1.31]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.23 [1.15;1.31]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.29 [1.18;1.41]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePrimary school or lower\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.39 [1.32;1.47]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.42 [1.34;1.51]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.41 [1.33;1.49]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.43 [1.33;1.55]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMarital status (Ref.: unmarried)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMarried\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.80 [0.74;0.86]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.79 [0.73;0.86]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.81 [0.75;0.87]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.81 [0.73;0.90]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDivorced or widowed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.14 [1.04;1.26]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.10 [0.99;1.23]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.12 [1.02;1.24]*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.16 [1.02;1.32]*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEmployment status (Ref.: jobless)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStudent\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.06 [0.94;1.19]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.01 [0.87;1.16]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.03 [0.91;1.18]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.10 [0.93;1.31]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEmployed or retired\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.65 [0.59;0.71]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.67 [0.60;0.74]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.65 [0.59;0.71]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.71 [0.62;0.80]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSelf-employed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.77 [0.72;0.83]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.81 [0.74;0.87]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.79 [0.73;0.86]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.86 [0.78;0.95]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eInformal worker\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.85 [0.80;0.89]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.81 [0.77;0.86]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.86 [0.81;0.90]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.85 [0.79;0.91]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.87 [0.80;0.94]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.85 [0.78;0.93]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.89 [0.82;0.96]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.93 [0.84;1.03]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMonthly household expenditure (Ref.: 1st quintile)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2nd quintile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.93 [0.88;0.99]*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.96 [0.90;1.02]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.93 [0.88;0.99]*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.95 [0.88;1.03]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3rd quintile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90 [0.85;0.95]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.92 [0.86;0.98]*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90 [0.85;0.96]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.93 [0.86;1.01]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4th quintile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.82 [0.77;0.87]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.84 [0.78;0.90]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.84 [0.79;0.89]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.86 [0.79;0.94]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5th quintile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.76 [0.71;0.81]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.79 [0.74;0.86]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.77 [0.72;0.83]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.81 [0.74;0.89]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSmoking status (Ref: every day)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNot every day\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.10 [1.00;1.22]*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.10 [0.99;1.23]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.08 [0.98;1.20]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.11 [0.98;1.27]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFormer smoker\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.23 [1.12;1.35]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.22 [1.10;1.36]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.24 [1.13;1.37]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.15 [1.00;1.31]*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNever smoked\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.66 [0.61;0.71]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.68 [0.63;0.74]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.65 [0.61;0.71]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.66 [0.60;0.73]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAlcohol use (Ref.: under standard)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMore than standard\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.99 [0.86;1.13]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.95 [0.81;1.10]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.99 [0.86;1.13]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90 [0.75;1.09]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo alcohol\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.59 [0.54;0.66]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.59 [0.53;0.66]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.58 [0.52;0.64]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.57 [0.50;0.66]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePhysically active\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90 [0.85;0.96]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90 [0.84;0.96]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90 [0.84;0.96]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.93 [0.85;1.02]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with lung tuberculosis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.11 [1.73;2.56]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.22 [1.79;2.74]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.05 [1.66;2.52]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.22 [1.71;2.89]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with hypertension\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.35 [1.27;1.44]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.36 [1.27;1.46]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.36 [1.28;1.46]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.36 [1.25;1.49]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with stroke\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.50 [2.22;2.82]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.41 [2.10;2.76]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.41 [2.12;2.74]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.35 [1.97;2.80]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with diabetes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.67 [1.51;1.86]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.77 [1.57;1.98]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.69 [1.51;1.89]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.84 [1.59;2.12]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with heart disease\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.66 [1.49;1.85]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.62 [1.43;1.83]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.65 [1.46;1.85]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.48 [1.26;1.74]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with asthma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.06 [1.89;2.25]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.10 [1.91;2.31]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.10 [1.92;2.30]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.22 [1.97;2.50]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with rheumatoid arthritis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.78 [1.68;1.89]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.80 [1.69;1.92]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.75 [1.65;1.87]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.71 [1.57;1.85]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with cancer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.40 [1.88;3.08]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.64 [2.02;3.46]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.28 [1.73;2.99]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.62 [1.84;3.71]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEver diagnosed with kidney failure\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.87 [1.51;2.31]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.11 [1.68;2.65]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.91 [1.52;2.38]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.03 [1.54;2.69]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFamily member with psychosis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.14 [1.85;2.49]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.13 [1.81;2.51]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.18 [1.86;2.56]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.10 [1.69;2.61]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDifficulty to access healthcare\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.45 [1.38;1.53]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.41 [1.33;1.49]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.45 [1.38;1.53]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.44 [1.35;1.54]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIntercept\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.11 [0.10;0.13]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.12 [0.10;0.13]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.11 [0.09;0.13]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.11 [0.09;0.13]**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNumber of observations\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e175078\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e138286\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e155488\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e84498\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e** p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.01;* p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003eThe association of household expenditure with depression varied across the categories of residential areas. Within coastal abrasion areas, significant associations were found within the third to fifth quintiles, while in tidal flooding areas, significant associations were found within the fourth and fifth quintiles. The findings show that households within the poorest quintiles are more likely to have depression. In all categories of residential areas, ex-smokers were more likely to have depression than regular smokers (OR\\u0026thinsp;=\\u0026thinsp;1.15\\u0026ndash;1.24). In contrast, non-smokers were less likely to have depression than regular smokers (OR\\u0026thinsp;=\\u0026thinsp;0.66\\u0026ndash;0.68). Those who reported not drinking alcohol were less likely to have depression than those who consumed alcohol. Respondents who were physically active were less likely to have depression than those who were less active (OR\\u0026thinsp;=\\u0026thinsp;0.90\\u0026ndash;0.93). In all models, individuals with lung tuberculosis, hypertension, stroke, diabetes mellitus, heart disease, asthma, rheumatoid arthritis, cancer, and kidney failure were more likely to have depression than those without these illnesses. The greatest odds of depression in the illness category were found among respondents who reported having cancer (OR\\u0026thinsp;=\\u0026thinsp;2.63). People with difficulty accessing healthcare were also more likely to have depression (OR\\u0026thinsp;=\\u0026thinsp;1.41\\u0026ndash;1.45).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec12\\\"\\u003e\\n \\u003ch2\\u003eMarginal effects of healthcare access and household expenditure\\u003c/h2\\u003e\\n \\u003cp\\u003eMarginal effect analyses were employed to examine the predicted probability of having depression with healthcare access and household expenditure as outcome variables. \\u003cstrong\\u003eSupplementary Fig.\\u0026nbsp;1\\u003c/strong\\u003e shows the marginal effect of healthcare access based on each type of coastal hazard. Controlled for socio-demographic characteristics and types of illness, respondents who had difficulty accessing healthcare were more likely to have depression than those with less difficulty accessing healthcare. \\u003cstrong\\u003eSupplementary Fig.\\u0026nbsp;2\\u003c/strong\\u003e describes the marginal effects of household expenditure based on each type of coastal hazard. The results highlight that individuals belonging to the poorest households were more likely to have depression.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study is among the first to address the effect of sea level rise on depression using nationally representative data from Indonesia. Nearly one-third of the respondents to the survey lived in areas affected by coastal hazards, and the prevalence of depression was higher among them (6.7%) than among those not affected by coastal hazards (5.7%). Living in a coastal hazard area is correlated with 1.13 higher odds of having depression. This finding supports the hypothesis that climate change-induced events can impact mental health, consistent with existing evidence of the mental health impacts from rising sea levels in the Solomon Islands,\\u003csup\\u003e15\\u003c/sup\\u003e the United States\\u003csup\\u003e29\\u003c/sup\\u003e and the Pacific Islands.\\u003csup\\u003e30\\u003c/sup\\u003e Using data from two coastal counties in the US, Monsour and colleagues found that individuals affected by tropical cyclones were at higher risk of major depressive disorders (coefficients between 2.2 and 2.8).\\u003csup\\u003e29\\u003c/sup\\u003e A study in the Pacific Islands interviewed 100 Tuvalian participants and revealed that 62% of them experienced psychological distress.\\u003csup\\u003e30\\u003c/sup\\u003e Asugeni and colleagues interviewed 57 individuals living in a remote coastal region of the Solomon Islands. They showed that 90% of them stated that they feared and worried about the impact of sea level rise.\\u003csup\\u003e15\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eSea level rise may affect mental health directly by exposing people to trauma. Rising sea levels cause shoreline abrasion\\u003csup\\u003e31\\u003c/sup\\u003e and escalate the risk of coastal flooding.\\u003csup\\u003e32\\u003c/sup\\u003e Our findings showed that individuals living in areas with coastal abrasion, hurricanes, and tidal flooding were 1.16, 1.14, and 1.24 times more likely to have depression than those living outside these areas. A prior study using data from the English National Study of Flooding and Health revealed that flooding was associated with 7.77, 4.16 and 14.70 higher odds of depression, anxiety and PTSD, respectively.\\u003csup\\u003e33\\u003c/sup\\u003e The risks of depression among people affected by extreme events in our study are lower than the study from the UK, probably due to the lower prevalence of depression in the national survey in Indonesia used in this study (6%) compared to the prevalence of depression in the UK (16%).\\u003csup\\u003e34\\u003c/sup\\u003e Depression and anxiety scores among individuals affected by seasonal floods in Northern India have also been found to be higher than those not affected.\\u003csup\\u003e35\\u003c/sup\\u003e The effects of hurricanes on mental health have been documented in several studies.\\u003csup\\u003e36 37\\u003c/sup\\u003e For example, Kohn assessed PTSD and major depressive disorder in hurricane-affected adults at two months and two years after the event.\\u003csup\\u003e36\\u003c/sup\\u003e The study found that the prevalence of both diseases has remained generally steady over time (PTSD 10.6% and 11.8%, major depressive disorder 19.5% and 19.4% at two months and two years). The impact of hurricanes has also been found to be persistent in New York City and Long Island residents.\\u003csup\\u003e37\\u003c/sup\\u003e Having depression and anxiety immediately after a hurricane was a predictor of persistent depression and anxiety a year later.\\u003c/p\\u003e \\u003cp\\u003eCoastal hazards also lead to the physical loss of land and dwellings, food and water shortages, loss of employment, and eventual displacement, including migration. Munro and colleagues found a strong link between displacement following the 2013-14 floods in the UK and the prevalence of depression, anxiety, and PTSD one year later.\\u003csup\\u003e38\\u003c/sup\\u003e A link between displacement from one\\u0026rsquo;s home after a climate-related disaster and growing mental health symptoms has also been found in Bangladesh.\\u003csup\\u003e39\\u003c/sup\\u003e Coastal hazards can also affect mental health indirectly by affecting physical health and community well-being. Sea level rise is associated with increased exposure to waterborne pathogens, vector-borne diseases, saltwater intrusion, and poor air quality due to mould.\\u003csup\\u003e40\\u003c/sup\\u003e For example, the sea level change had a positive and significant association with a higher prevalence of dengue disease in Malaysia.\\u003csup\\u003e41\\u003c/sup\\u003e Poor physical health may thus increase the risk of depression among people living in coastal communities.\\u003c/p\\u003e \\u003cp\\u003eOur findings further identify that women, those with low educational attainment, informal workers, those with lower incomes, those with comorbidities and those with difficulties accessing healthcare were among the groups with a higher risk of depression when exposed to coastal hazards. Women may be at increased risk of depression due to gender-differentiated social roles and a lack of access to resources. A study in coastal Bangladesh looked at Hurricane Aila\\u0026rsquo;s effects in 2009 and found that women could not attend nongovernmental organisation (NGO) training or income-generating activities without their husbands\\u0026rsquo; approval due to gender roles. The researchers also discovered that labouring in the fields in the rising heat, extracting water from seawater-contaminated wells, and damaged infrastructure by recurring tidal flooding made it more difficult for women to obtain everyday resources, which led to increased levels of hunger.\\u003csup\\u003e42\\u003c/sup\\u003e Rising sea levels inundate coastal areas, swallowing up agricultural land in Indonesia. This land loss directly impacts food production, especially rice, a vital crop for Indonesia. Studies estimate millions of hectares of farmland could be lost.\\u003csup\\u003e43\\u003c/sup\\u003e A review showed that women suffer more detrimental impacts of climate change because of social and cultural norms regarding gender, such as women tend to have less power in decision-making in the family, as well as a lack of access to and control over assets, with some exceptions.\\u003csup\\u003e44\\u003c/sup\\u003e Literature has highlighted that poverty is a major factor influencing people\\u0026rsquo;s vulnerability to climate-related shocks and stressors,\\u003csup\\u003e45\\u003c/sup\\u003e and thus may be at higher risk of mental disorders. According to a study in Bangladesh, having an unpaid job or suffering considerable income loss during Cyclone Amphan was significantly associated with higher psychological distress symptoms.\\u003csup\\u003e39\\u003c/sup\\u003e Climate-related disasters, including sea level rise, may destroy crops, land, and critical infrastructure. These lead to reduced agricultural output and increased prices of major crops, greatly impacting food production and threatening food security.\\u003csup\\u003e46\\u003c/sup\\u003e Lower output of crops means lower incomes for the most vulnerable people. Under these conditions, the poorest people will be those most affected by sea level rise as they already use most of their incomes for food and require additional income to meet their daily nutritional requirements. Several South Asian countries, including Bangladesh, India, Pakistan and Nepal, launched cash transfer programs to help low-income families cope with climate-related disasters and income losses.\\u003csup\\u003e47\\u003c/sup\\u003e Indonesia\\u0026rsquo;s government also provides a cash transfer program for poor people called Direct Cash Assistance (\\u003cem\\u003eBantuan Langsung Tunai\\u003c/em\\u003e) program. However, little is known regarding the effectiveness of this program in helping poor communities cope with climate change.\\u003c/p\\u003e \\u003cp\\u003eThere are some limitations to this study that should be noted. First, our study is cross-sectional, which implies important limitations to the interpretability of data in terms of finding causal effects between variables. Second, the only measure available is depression. More severe mental disorders, including PTSD, acute stress disorders, anxiety, substance use and suicide, may occur as a result of the impact of climate change-related disasters.\\u003csup\\u003e3\\u003c/sup\\u003e Our data was taken in 2018 and needs to be updated with the rapid changes in the environment due to climate change. Future studies, including more mental disorders, could better capture the consequences of sea level rise on mental health. Targeted coproduced qualitative works are also required to integrate and refine the mechanisms of action and most modifiable intervention points.\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, living in areas affected by coastal hazards in Indonesia is related to higher odds of having depression, and almost one-third of the Indonesian population (31.3%) live in these areas. It is thus important to implement interventions to mitigate the impact of sea level rise on mental health and well-being. So far, the available adaptation strategies for sea level rise have been focused on preventing land damage, such as building sea walls, reforestation, upgrading existing drainage infrastructure, and preventing socio-economic damage due to the loss of land.\\u003csup\\u003e48 49\\u003c/sup\\u003e Our findings highlight the importance of further research on interventions to address the impact of rising sea levels on mental health. Based on our findings, the mental health of women, those with informal jobs, low education and less income, those with comorbidities and those with difficulty accessing healthcare are more likely to be affected by sea level rise. In addition, pre-existing health burdens make people more vulnerable to sea-level effects by reducing resources. Therefore, we need to continue to emphasise investment in mental health and non-communicable disease management to ensure resilience in this impending context. Adaptation strategies and mental health interventions should thus target these groups.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAUTHOR CONTRIBUTIONS\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA.M.: conceptualisation, methodology, data curation, writing\\u0026mdash;original draft, writing\\u0026mdash;review and editing, funding acquisition. S.S.: methodology, data curation, writing\\u0026mdash;original draft, writing\\u0026mdash;review and editing, funding acquisition. H.S., H.B., P.B.: writing\\u0026mdash;review and editing, funding acquisition.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFUNDING STATEMENTS\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research was funded by the Faculty of Nursing at Universitas Indonesia (Award ID NKB-217/UN2.RST/HKP.05.00/2024)]. The views expressed in this publication are those of the author(s) and not necessarily those of the funder.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDATA AVAILABILITY STATEMENT\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIndonesia Basic Health Survey (Riset Kesehatan Dasar or Riskesdas) data are available through the Health Policy and Development Agency, Ministry of Health, Republic of Indonesia, at https://layanandata.kemkes.go.id/. The form for data requests can be found in https://layanandata.kemkes.go.id/request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCOMPETING INTEREST:\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eWorld Health Organization. Mental health and climate change: Policy brief. 2022.\\u003c/li\\u003e\\n\\u003cli\\u003eWorld Meteorological Organization. Atlas of Mortality and Economic Losses from Weather, Climate and Water-related Hazards 2023, [Available from: https://wmo.int/publication-series/atlas-of-mortality-and-economic-losses-from-weather-climate-and-water-related-hazards].\\u003c/li\\u003e\\n\\u003cli\\u003eLawrance, E., Thompson, R., Fontana, G. \\u0026amp; Jennings, N. The impact of climate change on mental health and emotional wellbeing: current evidence and implications for policy and practice. \\u003cem\\u003eGrantham Institute briefing paper\\u003c/em\\u003e \\u003cstrong\\u003e36\\u003c/strong\\u003e, 1-36 (2021).\\u003c/li\\u003e\\n\\u003cli\\u003eCharlson, F., Ali, S., Benmarhnia, T., Pearl, M., Massazza, A., Augustinavicius, J., \\u0026amp; Scott, J. G. Climate change and mental health: A scoping review. \\u003cem\\u003eInternational Journal of Environmental Research and Public Health\\u003c/em\\u003e \\u003cstrong\\u003e18\\u003c/strong\\u003e(9), 4486 (2021).\\u003c/li\\u003e\\n\\u003cli\\u003eBundo, M., de Schrijver, E., Federspiel, A., Toreti, A., Xoplaki, E., Luterbacher, J., ... \\u0026amp; Vicedo-Cabrera, A. M. Ambient temperature and mental health hospitalizations in Bern, Switzerland: A 45-year time-series study. \\u003cem\\u003ePLoS One\\u003c/em\\u003e \\u003cstrong\\u003e16\\u003c/strong\\u003e(10), e0258302 (2021).\\u003c/li\\u003e\\n\\u003cli\\u003eThawonmas, R., Hashizume, M., \\u0026amp; Kim, Y. Projections of temperature-related suicide under climate change scenarios in Japan. \\u003cem\\u003eEnvironmental Health Perspectives\\u003c/em\\u003e \\u003cstrong\\u003e131\\u003c/strong\\u003e(11), 117012 (2023).\\u003c/li\\u003e\\n\\u003cli\\u003eMajeed, H. \\u0026amp; Lee, J. The impact of climate change on youth depression and mental health. \\u003cem\\u003eThe Lancet Planetary Health\\u003c/em\\u003e \\u003cstrong\\u003e1\\u003c/strong\\u003e(3), e94-e95 (2017).\\u003c/li\\u003e\\n\\u003cli\\u003eHansen, A., Bi, P., Nitschke, M., Ryan, P., Pisaniello, D., \\u0026amp; Tucker, G. 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When the tide gets high: A review of adaptive responses to sea level rise and coastal flooding. \\u003cem\\u003eJournal of Environmental Planning and Management\\u003c/em\\u003e \\u003cstrong\\u003e63\\u003c/strong\\u003e(12), 2102-2143 (2020).\\u003c/li\\u003e\\n\\u003cli\\u003eBongarts Lebbe, T., Rey-Valette, H., Chaumillon, \\u0026Eacute;., Camus, G., Almar, R., Cazenave, A., ... \\u0026amp; Euzen, A. Designing coastal adaptation strategies to tackle sea level rise. \\u003cem\\u003eFrontiers in Marine Science\\u003c/em\\u003e \\u003cstrong\\u003e8\\u003c/strong\\u003e, 740602 (2021). \\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\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4442319/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4442319/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eClimate change has a profound impact on the mental health and well-being of people all over the world. However, studies on the impacts of climate-driven rising sea levels on mental health remain few. This study aims to examine the risk of depression among people who live in coastal areas susceptible to the natural hazards associated with climate change. We used the Indonesia Basic Health Survey 2018, which included 642,419 adults in Indonesia. Multivariable logistic regression analysis was conducted to examine the relationship between living in a coastal hazard area and depression. We included socio-demographics, health status, and health access information in the analysis to identify the most vulnerable groups. Our findings show that people who live in coastline hazard areas are 1.13 times more likely to have depression than people who live outside those areas. Individuals living in the coastal hazards areas who were less likely to have autonomous mobility or resources, including young adults, females, those with low socio-economic conditions, and those with pre-existing health conditions, had a higher risk of depression than other groups. Culturally acceptable and effective mental health interventions should thus target these vulnerable populations and settings to effectively reduce climate-related health risks.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Depression among people who live in coastal hazard areas in Indonesia: Evidence from a population-based national survey\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-06-07 23:16:32\",\"doi\":\"10.21203/rs.3.rs-4442319/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-10-22T06:17:42+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-06-10T12:48:52+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-06-10T00:59:16+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"65834735844521710056895941454009549708\",\"date\":\"2024-05-30T04:51:34+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"265715060258073699541097480184111905164\",\"date\":\"2024-05-29T13:10:40+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-05-27T13:05:34+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-05-27T12:16:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2024-05-24T04:12:57+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-05-24T04:11:43+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Scientific Reports\",\"date\":\"2024-05-18T20:10:09+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"0419b3bf-ae17-40e5-a418-bc1b2cb11fbc\",\"owner\":[],\"postedDate\":\"June 7th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-03-03T16:02:02+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-4442319\",\"link\":\"https://doi.org/10.1038/s41598-025-89298-1\",\"journal\":{\"identity\":\"scientific-reports\",\"isVorOnly\":false,\"title\":\"Scientific Reports\"},\"publishedOn\":\"2025-02-27 15:57:53\",\"publishedOnDateReadable\":\"February 27th, 2025\"},\"versionCreatedAt\":\"2024-06-07 23:16:32\",\"video\":\"\",\"vorDoi\":\"10.1038/s41598-025-89298-1\",\"vorDoiUrl\":\"https://doi.org/10.1038/s41598-025-89298-1\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4442319\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4442319\",\"identity\":\"rs-4442319\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}