Housing Conditions, Neighbourhood Satisfaction and Suicide Mortality in Belgium: A Population-Based Cohort Study

Housing Conditions, Neighbourhood Satisfaction and Suicide Mortality in Belgium: A Population-Based Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Housing Conditions, Neighbourhood Satisfaction and Suicide Mortality in Belgium: A Population-Based Cohort Study Joan Damiens This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8338834/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Housing and neighbourhood conditions are increasingly recognised as structural determinants of mental health, yet their role in suicide mortality remains poorly quantified. We examined how housing quality and neighbourhood satisfaction are associated with suicide risk in the Belgian working-age population, and whether these associations vary across life stages or according to concordance between housing and neighbourhood conditions. Methods We used linked data from the 2001 Belgian Census, the National Register and cause-of-death records to construct a population-based cohort of all adults aged 25–69 years (N = 2.8 million) followed for suicide mortality between 2002 and 2006. Housing and neighbourhood quality scores were derived using principal component analysis from detailed census items on dwelling characteristics, installations and satisfaction with the immediate environment and local services. We estimated sex-specific Cox proportional hazards models for suicide, adjusting for demographic and socioeconomic characteristics, subjective health, residential duration and municipal deprivation. We tested interactions with age group and neighbourhood satisfaction and conducted robustness checks excluding imputed data, restricting to respondents in good self-rated health, and applying Fine-Gray competing-risks models. Results Compared with living in the lowest quartile of housing quality, residence in the highest quartile was associated with a 13% lower suicide hazard among men and a 21% lower hazard among women, net of individual and contextual covariates. Neighbourhood satisfaction showed only weak overall associations with suicide risk. However, among individuals living in good-quality housing, higher neighbourhood satisfaction was associated with lower suicide hazards, whereas among those in poor-quality housing, neighbourhood satisfaction did not attenuate the elevated suicide risk. We found no clear evidence that associations between housing quality and suicide differed across adult life stages. Robustness analyses yielded similar patterns. Conclusions In early-2000s Belgium, poor housing conditions were strongly associated with higher suicide mortality, independently of conventional sociodemographic risk factors. Neighbourhood advantages appeared protective only when basic housing adequacy was ensured, and did not compensate for poor housing. Suicide prevention and mental health policy should therefore consider housing quality as a key determinant, alongside efforts to improve neighbourhood environments. Trial registration not applicable Housing conditions Suicide Neighbourhood quality Register data Belgium Figures Figure 1 Figure 2 Contributions to the literature Uses population-wide linked census and mortality data to quantify how detailed housing conditions are associated with suicide mortality, beyond individual risk factors. Shows that neighbourhood advantages benefit suicide risk primarily among people living in good-quality housing, while neighbourhood satisfaction does not offset the elevated suicide risk associated with poor housing conditions. Highlights housing quality as a key determinant for suicide prevention and supports integrated housing and mental health policies that prioritise the most deprived housing sectors. Methods Data sources and study population Our main data source is the 2001 Belgian socioeconomic survey (2001 Belgian Census). Self-administered questionnaires were sent in autumn 2001 to the entire population living in Belgium (10,296,349 individuals), and it is estimated that 3.1% did not respond even after at least three reminders [ 47 ]. Although these data refer to the early 2000s, the 2001 Census remains one of the most detailed and underused sources of information on living conditions in Belgium. It provides population-level information on housing characteristics, satisfaction with the immediate environment and subjective health. One reference person in each household answered the questions on housing and neighbourhood conditions. The Appendix (Tables A1-A2) presents the housing- and neighbourhood-related items. The 2001 Census was linked to the National Register and death certificates from 1 January 2002 to 31 December 2006, by Statistics Belgium (pseudo-anonymised data). Death certificates provide information on the date of death and the underlying, intermediate and immediate causes of death, coded using the 10th International Classification of Diseases (ICD-10). We excluded people living in collective households (care facilities, hospitals, monasteries, military compounds, prisons, etc.), who represented about 1% of the population in 2001, and restricted the analysis to adults aged 25–69 years. Mental health problems in younger and older populations often follow distinct trajectories: among younger individuals, onset of conditions such as schizophrenia, bipolar disorder or eating disorders – which are linked to suicide risk – is more common [ 48 ], whereas older adults are more affected by declining physical health, loss of mobility, chronic pain and reduced social contact [ 49 ]. In these age groups, housing conditions may play a less direct role in mental health, as they often reflect other underlying circumstances, such as parental socioeconomic status for adolescents and young adults, or the health status of elderly individuals. Measures Suicide Suicide includes all deaths due to intentional self-harm, i.e. deaths coded X60-X84 and Y87.0 in ICD-10, whether this cause is recorded as underlying, intermediate or immediate. Between 2002 and 2006, among individuals aged 25–69 in 2001, we observe 88,470 deaths for men, including 5,158 suicides, and 48,903 deaths for women, including 2,030 suicides. Housing- and neighbourhood-related indicators We constructed housing and neighbourhood quality scores from multiple indicators using principal component analysis (PCA). Following a previous study [ 50 ], we retained the first component with the highest eigenvalue and defined this as the housing or neighbourhood quality score. The Appendix (Tables A1-A2) lists all items, and Tables A3-A4 report the PCA results. The Census provides information on general and basic dwelling characteristics and the quality of installations. Based on these indicators, we derived a housing quality score for each household and assigned it to all its members. The score was then divided into quartiles (very low, low, high, very high) and, for interaction analyses, further collapsed into two groups at the median (low and high). Because the distribution is skewed towards better scores, these two groups can be interpreted as low-intermediate (low) and high-excellent (high) housing quality. The scoring procedure gives higher weights to basic facilities (bathroom, central heating) and indoor items (quality of inside walls, electrical system, windows and presence of double glazing) than to outdoor items (quality of outside walls, pipes and roof). Lower weights are assigned to more general housing characteristics (type of building, presence of a garden, overcrowding). Neighbourhood-related indicators capture the general characteristics of the surroundings and the availability and quality of local services. We constructed a neighbourhood quality score from these items and divided it into thirds of the population (low, intermediate, high). Higher weights were given to more subjective elements (aesthetics, cleanliness, quietness), whereas service-related items contributed less to the overall score. Covariates The models adjust for demographic and socioeconomic covariates. Suicide risk varies strongly with age (Appendix, Figure A1). We also account for marital and parental status, measured through household composition, distinguishing unpartnered individuals from those in marital and non-marital unions, with and without children in the household. Non-marital unions are defined as households with exactly two unrelated opposite-sex adults with an age difference of less than 15 years, following Belgian demographic research [ 51 ]. We further adjust for citizenship (Belgian, other European, non-European) and urbanicity of the municipality of residence (urban, suburban, rural), which are associated with suicide [ 52 , 53 ]. Belgian regions (Flanders, Wallonia, Brussels) differ in housing policies and cultural characteristics, including higher religiosity in Flanders, and are therefore included as covariates. Socioeconomic covariates include educational attainment (primary, lower secondary, upper secondary, higher/tertiary) and occupational status (unemployed, inactive, employed, self-employed). We add the number of years spent in the current dwelling as an approximation of residential attachment and satisfaction [ 54 ]. Finally, we include the Belgian Index of Multiple Deprivation (BIMD) [ 55 ], a multidimensional measure of municipal socioeconomic conditions based on employment, education, income and crime (excluding the housing component), constructed from the 2001 Census and categorised into deciles of municipalities. A word on missing values In the 2001 Census, 5.2% of respondents did not answer any of the questions on housing or neighbourhood conditions, and 14.9% did not respond to at least half of them. Non-respondents in the Census have a higher mortality risk, comparable to that of the most underprivileged groups (Bourguignon et al., 2022), and a higher risk of suicide in 2002 (46.4 per 100,000 among non-respondents vs. 24.3 per 100,000 in the general population). To limit data loss while reducing bias, we first allowed individuals to have at most three missing items per score. For those with up to three missing items, we used group-mean imputation. For the housing quality score, we assigned the mean score of individuals with the same educational level, occupational status, household composition, nationality and region of residence. For the neighbourhood quality score, we assigned the mean score of the statistical sector – the smallest geographical unit in Belgium, with an average of around 500 inhabitants. Individuals with more extensive missingness were excluded from the analyses. Statistical analysis Main analysis We first describe associations between suicide rates, housing and neighbourhood scores, and independent variables using analyses of variance (ANOVA). These tests are used purely descriptively to compare crude rates and mean scores across groups. To control for potential confounders, we estimate Cox proportional hazards models to assess the risk of suicide among adults aged 25–69 years living in Belgium between 2002 and 2006. The hazard function is specified as \(\:\begin{array}{c}h\left(t,X\right)={h}_{0}\left(t\right)\:exp\left(\sum\:_{i=1}^{p}{\beta\:}_{i}{X}_{i}\right)\#\left(1\right)\end{array}\) here \(\:{h}_{0}\left(t\right)\:\) is the baseline hazard at the beginning of the observation, X is the vector of the p covariates of the model and \(\:{\beta\:}_{\:}\) are the regression coefficients. The time scale is time since baseline (1 January 2002). All individuals alive and resident in Belgium on 1 January 2002 enter the risk set. Follow-up runs to 31 December 2006. Individuals are right-censored at the time of death from other causes, emigration, or residential moves within Belgium, because the housing information collected in 2001 no longer reflects their current dwelling. We assessed the proportional hazards assumption for all covariates and found no evidence of violation. To investigate interactions between housing quality and neighbourhood satisfaction by age group, we compute predicted hazard ratios of suicide based on the Cox models. These are independent of the baseline hazard and are given by $$\:\begin{array}{c}\widehat{hr}=\:\frac{\widehat{h\left(t,X\right)}}{{h}_{0}\left(t\right)}=\text{exp}\widehat{\beta\:}X\#\left(2\right)\end{array}$$ Because we analyse population data rather than a sample, the use of inferential statistics warrants caution [ 56 ]. We nevertheless report 95% confidence intervals as measures of precision, keeping in mind that the rarity of the outcome may lead to wide intervals. In interpreting results, we consider both statistical significance (confidence intervals) and the magnitude of differences in estimates between categories (substantive significance) [ 57 , 58 ]. Robustness checks Models estimated without imputation of the housing and neighbourhood scores are presented in the Appendix (Table A5) and yield results similar to those of the main analyses. To address a major confounder, predisposition to poor physical and mental health – which is strongly associated with both housing careers [ 59 ] and suicide mortality [ 11 ] – we re-estimate the models in the subsample of individuals who reported good or very good health at baseline (Appendix, Table A6). This subsample is less likely to include individuals with severe health limitations at the start of follow-up. Differences in results between the main and restricted samples are discussed in the Discussion section. We additionally estimate Fine-Gray subdistribution hazard models [ 60 ], treating death from other causes and residential mobility as competing events (Appendix, Tables A7–A8). Instead of censoring individuals who die from other causes or move, these models account for the possibility that such events are alternative outcomes to suicide that could bias our estimates if ignored. The subdistribution hazard ratios obtained from these models are closely aligned with the Cox estimates, suggesting that informative censoring due to mortality or mobility is unlikely to explain the observed associations. Background Since the 1990s, suicide rates in Europe have decreased, but they remain high in Belgium. In 2021, Belgium recorded 15.4 suicides per 100,000 inhabitants (19.4 in 2005), compared with 10.2 per 100,000 in the European Union (13.3 in 2005) [ 1 – 3 ]. Extensive research has examined determinants of suicide such as education [ 4 ] and marital status [ 5 ]. In contrast, the role of the living environment – defined here by housing and neighbourhood quality – in suicide mortality remains poorly understood and has rarely been quantified using population-based data. Housing conditions are recognised as important determinants of physical health and longevity [ 6 , 7 ], and their relationship with mental health and wellbeing has been documented [ 8 , 9 ], often using a limited set of indicators such as household density or building type. While this work shows that the home environment matters for mental health, much less is known about how housing and neighbourhood quality relate to suicide. Previous studies have also suggested that the association between housing tenure and suicide varies over the life course, being strongest in mid-life when pressures to achieve homeownership are highest [ 10 ]. Testing whether the association between living environment quality and suicide differs across life stages may therefore help to clarify the mechanisms linking housing to suicidal behaviour, for instance, if pressures to secure good-quality housing cumulate with pressures to become a homeowner. Finally, existing literature has not yet investigated how relative inequalities in living conditions, and in particular discordance between housing and neighbourhood quality – living in low-quality housing in an otherwise good neighbourhood, or vice versa – might affect suicide risk. In this article, we use linked census-register data covering the entire Belgian population aged 25–69 years to (1) quantify the association between housing conditions and suicide mortality; (2) test whether this association varies across adult life stages; and (3) examine how concordance and discordance between housing quality and neighbourhood satisfaction relate to suicide risk. By focusing on suicide mortality rather than self-reported mental health outcomes, and by jointly modelling housing and neighbourhood conditions, we contribute to population research on mortality, population health and human-environment interactions. Housing and mental health The most well-established suicide determinants are depression and previous suicidal attempts [ 11 ]. Housing stability [ 12 ] and tenure [ 13 ] have also been linked to suicide, with tenants showing higher risks than owners. Beyond this aspect of residential stability, however, much less is known about how characteristics of the close living environment relate to suicide mortality. In contrast, a substantial literature has examined how housing and neighbourhood conditions can shape other mental health outcomes such as life satisfaction, depression and suicidal ideation. Given the strong links between life satisfaction, mental health and suicide mortality [ 14 , 15 ], we use this work to define our hypotheses. Life satisfaction – one’s appraisal of their life – can protect against suicidal thoughts and behaviours by supporting feelings of belonging, hope and trust in the future [ 15 ]. Depression and other mental health problems may mediate the pathway from low life satisfaction to suicidal behaviour by fostering a sense of failure and negative self-perception [ 16 ]. Among the determinants of life satisfaction, the living environment is central, as it is an immediate reflection of one’s socioeconomic achievements and material comfort. Since the 1990s, the Belgian constitution has recognised access to decent housing as a fundamental right and a cornerstone of human dignity. Previous studies show that poor housing characteristics, such as dwelling type, age, floor level, small surface area and low overall comfort, are associated with worse mental health and wellbeing [ 8 , 9 ]. Living in a high-rise building [ 8 , 9 ] or in overcrowded dwellings [ 17 ] are associated with a higher risk of depressive symptoms and anxiety. Drawing on Learned Helplessness theory, low control over one’s environment and persistent discomfort are likely to increase risks of depression and psychological distress [ 18 ], especially for low-income households who may feel trapped in unsatisfactory housing because moving is difficult, costly and demanding. Quantitative studies often rely on a limited set of housing indicators and adjust insufficiently for socioeconomic characteristics, whereas qualitative work highlights a broader range of conditions – noise, damp, and inability to keep the home warm – that are associated with lower life satisfaction and wellbeing [ 19 – 21 ]. One South Korean population-based panel study is particularly notable for using a more extensive set of housing variables [ 22 ]. It shows that functional issues (ventilation, noise, heating, lighting) increase depression and suicidal ideation among middle-aged adults, while structural problems (building permanence, material quality, resistance to heat, fire and moisture) are more strongly related to depression in older adults. Residential satisfaction partly mediates the association with suicidal ideation, especially for young men. Because depression and severe psychological distress are major risk factors for suicidal behaviour, it is plausible that better housing quality also protects against suicide risk. We therefore hypothesise that higher housing quality is associated with a lower risk of suicide for both men and women, after accounting for demographic and socioeconomic characteristics (H1). Differences across life stages and gender Previous research suggests that the relationship between housing conditions and general health may intensify with age [ 23 ], and that associations between living environments and mental health can vary across the life course. A British study, for example, found that the relationship between green space and well-being changed with age, decreasing for men and increasing for women [ 24 ]. For suicidal thoughts and behaviours, evidence points to a somewhat different pattern. In South Korea, housing problems related to ventilation, noise, heating or lighting increased the likelihood of suicidal ideation mainly among middle-aged adults [ 22 ]. Similarly, Belgian research showed that the association between homeownership and suicide was most pronounced in mid-life [ 10 ]. Middle adulthood is a period when worry, dissatisfaction, job demands and insecurity are often at their highest [ 25 ], while the desire for stability peaks [ 26 ]. A mismatch between expectations and realities – regarding housing conditions, for instance – may undermine feelings of belonging and trust in the future [ 27 ], two key determinants of suicidal behaviour [ 28 ]. In contrast, younger adults may have more flexible housing pathways [ 29 ], and older adults may show greater resilience to adversity [ 30 ]. We therefore hypothesise that the protective association of housing quality with suicide risk differs across adult life stages and is strongest in mid-life (around ages 40–54) (H1b). Neighbourhood quality and mental health At the neighbourhood level, the presence of green spaces is associated with better mental health [ 24 , 31 , 32 ] as are higher perceived cleanliness [ 33 ] and better air quality [ 34 , 35 ]. However, the literature is not univocal: some papers find no clear association between the neighbourhood conditions and mental health [ 36 ] or suicidal behaviour once individual composition is taken into account [ 37 ]. The relationship between access to local services and mental health has been less studied, but existing work suggests that a better perception of service provision is associated with better mental health, particularly in more deprived communities [ 33 ], and that inequalities in the distribution of mental healthcare providers and other area resources are related to unmet mental healthcare needs and suicide mortality [ 38 ]. Because poor mental health is a major risk factor for suicidal behaviour, it is plausible that living in a neighbourhood that is perceived as clean, safe, well-served and generally satisfactory also reduces suicide risk. We therefore hypothesise that higher neighbourhood satisfaction is associated with a lower risk of suicide, net of individual demographic and socioeconomic characteristics and housing quality (H2). Discordance between housing and neighbourhood quality and mental health The interaction between housing and neighbourhood conditions represents an important yet still understudied dimension of suicide risk. A study conducted in eight European cities found that, in low-quality neighbourhoods, the association between housing quality and psychological well-being was stronger than in higher-quality neighbourhoods, suggesting an accumulation of detrimental elements in the close environment [ 39 ]. However, other work has not shown that better neighbourhood quality can offset the negative association between poor housing and low well-being [ 40 ]. According to the compound disadvantage hypothesis, the most vulnerable groups – such as those in poor housing – are likely to suffer most from adverse neighbourhood conditions [ 41 , 42 ], and lower-income households are also more dependent on local neighbourhood resources and less able to draw on opportunities outside their residential area [ 43 ]. Yet living in a high-quality neighbourhood is not necessarily beneficial for those in poor-quality housing, especially when their circumstances are discordant with those of their neighbours. Relative deprivation theory posits that individuals evaluate their situation relative to those around them [ 44 ]. Individuals living in substandard housing within otherwise affluent or high-quality neighbourhoods may experience heightened feelings of relative deprivation, stigma and social exclusion [ 45 , 46 ], which are linked to suicidal ideation and behaviour. Evidence that people report lower life satisfaction when neighbours’ incomes surpass their own, even after accounting for absolute income [ 45 ], supports the broader role of relative position in shaping mental health. We therefore hypothesise that the association between neighbourhood satisfaction and suicide depends on housing quality: among people living in good-quality housing, higher neighbourhood satisfaction is associated with lower suicide risk, whereas among those living in poor-quality housing, neighbourhood advantages do not reduce suicide risk (H3). By analysing suicide mortality in relation to housing and neighbourhood conditions in a whole-population cohort, this study contributes to understanding the structural determinants of suicide and provides evidence relevant for housing and mental health policy in Belgium and comparable welfare states. Results Descriptive Table 1 presents the crude suicide rates of the population according to the characteristics that will be included in the models. People living in high and, especially, very high housing quality present lower suicide rates than those living in low and very low housing quality. The population that is more satisfied with their neighbourhood also shows lower suicide rates. Suicide rates are lower among higher-educated groups. People in marital unions, as well as parents, present lower suicide rates compared to unpartnered individuals or persons in non-marital unions. People living in Brussels, homeowners and non-Belgians show lower suicide rates. When it comes to areas of residence (urban, suburban, rural), no clear differences in suicide rates are observed. Suicide rates are higher for individuals with poorer self-reported health, and longer residence duration in the current dwelling is associated with lower suicide risk. Table 1 also displays the means of the housing and neighbourhood quality scores. On average, higher educational attainment and more advantaged occupational statuses are associated with better housing conditions. People who graduated at most from lower secondary school and unemployed people present the lowest levels of neighbourhood satisfaction, while inactive populations are the most satisfied with their neighbourhood. Individuals in marital unions tend to have higher housing and neighbourhood quality, whereas one-person households and lone parents more often live in worse conditions. Living in Wallonia is associated with a lower mean housing score, while Flemish residents report the lowest neighbourhood satisfaction. Urban residents present the lowest means for both scores. Owners live in better quality housing and neighbourhoods than tenants, and Belgians have higher average housing and neighbourhood scores than non-Belgians. Poor self-reported health and shorter residence duration are also associated with worse housing and neighbourhood conditions. Table 1 Suicide rates, mean of housing quality score and mean of neighbourhood satisfaction score of the 25-69-year-old population living in Belgium, in the 2002–2006 period, according to individual characteristics, with results of ANOVA tests between suicide rates or means of scores and predictors. Suicide rate Housing quality score mean Neighbourhood satisfaction score mean Mean std err ANOVA Mean std err ANOVA Mean Std error ANOVA Housing quality Very low housing quality 0.00142 0.00003 **** Low 0.00144 0.00003 High 0.00102 0.00002 Very high 0.00084 0.00003 Neighbourhood satisfaction Low neighbourhood satisfaction 0.00129 0.00003 **** Intermediate 0.00126 0.00002 High 0.00113 0.00002 Education Primary education 0.00025 0.00004 **** 0.8161 0.0002 **** 0.8519 0.0002 **** Lower Secondary 0.00028 0.00003 0.8407 0.0002 0.8434 0.0002 Higher Secondary 0.00024 0.00002 0.8593 0.0002 0.852 0.0001 Higher 0.00018 0.00002 0.8881 0.0002 0.8569 0.0002 Activity status Unemployed 0.00023 0.00005 **** 0.7989 0.0004 **** 0.8321 0.0004 **** Inactive 0.00025 0.00005 0.83 0.0003 0.8612 0.0003 Employed 0.00019 0.00002 0.8595 0.0001 0.8515 0.0001 Self-employed 0.00026 0.00006 0.8825 0.0003 0.8595 0.0003 Household composition Married with children 0.00018 0.00002 **** 0.882 0.0001 **** 0.8596 0.0001 **** Married without children 0.00016 0.00002 0.8838 0.0001 0.8629 0.0002 Unmarried with children 0.00021 0.00006 0.8043 0.0004 0.8408 0.0004 Unmarried without children 0.00033 0.00007 0.807 0.0004 0.8405 0.0004 Lone-parent 0.00027 0.00006 0.7989 0.0003 0.8365 0.0003 Single without child 0.00005 0.00005 0.7894 0.0002 0.8397 0.0002 Other 0.00021 0.00006 0.8184 0.0004 0.8484 0.0004 Region Flanders 0.00025 0.00002 **** 0.8532 0.0001 **** 0.8486 0.0001 **** Wallonia 0.00025 0.00002 0.8471 0.0001 0.8584 0.0002 Brussels 0.00019 0.00003 0.8555 0.0002 0.8559 0.0002 Housing tenure Owner 0.0002 0.00002 **** 0.8937 0.0001 0.8625 0.0001 **** Tenant 0.00032 0.00003 0.7274 0.0002 0.8176 0.0002 Urbanicity of municipality of residence Urban 0.00025 0.00002 * 0.8216 0.0001 **** 0.8287 0.0001 **** Suburban 0.00024 0.00002 p < .05 0.8726 0.0001 0.8711 0.0001 Rural 0.00024 0.00003 0.8789 0.0002 0.8794 0.0001 Citizenship Belgian 0.00026 0.00002 **** 0.8572 0.0001 **** 0.8562 0.0001 **** Other European 0.00014 0.00004 0.821 0.0003 0.8346 0.0003 Non-European 0.00007 0.00004 0.7026 0.0006 0.8069 0.0006 Subjective health level Very good health 0.00014 0.00002 **** 0.8297 0.0001 **** 0.8704 0.0001 **** Good health 0.00019 0.00002 0.8157 0.0001 0.8584 0.0001 Intermediate health 0.00033 0.00004 0.7793 0.0002 0.8292 0.0002 Bad health 0.00068 0.00012 0.7501 0.0004 0.7977 0.0004 Very bad health status 0.00096 0.00029 0.7349 0.0008 0.7553 0.0001 Number of years spent in the dwelling Less than a year in the housing 0.00034 0.00005 **** 0.7608 0.0002 **** 0.8459 0.0002 **** 1–2 years 0.0003 0.00004 0.7727 0.0002 0.8447 0.0002 3–5 years 0.00026 0.00004 0.795 0.0002 0.8482 0.0002 > 5 years in the housing 0.00021 0.00002 0.8281 0.0001 0.8587 0.0001 Note for the ANOVA test: *: p < 0.05; **: p < 0.01; ***: p < 0.001; ****: p < 0.0001 Model Table 2 shows the results of the Cox proportional hazards models for suicide risk. Better housing quality is associated with lower suicide hazards for both men and women. For men, compared with the 25% of the population living in the worst housing conditions (first quartile of the housing score), living in very high housing quality (highest quartile) is associated with a 12.6% lower suicide hazard. For women, those living in very good housing conditions have a 20.9% lower suicide hazard than those in the poorest housing conditions. By contrast, for neighbourhood satisfaction, the estimated hazard ratios are close to 1 and the 95% confidence intervals include 1, so we do not find clear evidence of an association between the neighbourhood quality score and suicide risk. Table 2 – Cox proportional hazard model of the risk of suicide in 2002–2006 expressed in Hazard ratios (HR) and 95% confidence intervals. Men Women HR IC95% HR IC95% Housing quality (ref. Very low) Low housing quality 0.928 0.828 1.04 0.892 0.823 0.961 High housing quality 0.892 0.79 1.006 0.748 0.523 0.974 Very high (Q4) 0.874 0.765 0.998 0.791 0.633 0.987 Neighbourhood quality score (ref. Low) Intermediate 0.965 0.858 1.087 1.021 0.84 1.24 High 0.967 0.809 1.157 0.976 0.722 1.32 Age 0.984 0.979 0.988 0.977 0.969 0.985 Housing tenure (ref. Owner) Tenant 1.364 1.219 1.527 1.403 1.164 1.691 Unknown 1.634 1.025 2.606 1.375 0.569 3.325 Household composition (ref. Marital couple, children) Marital couple, no children 0.84 0.731 0.965 1.382 1.106 1.726 Non-marital couple, children 1.278 1.025 1.594 0.72 0.417 1.242 Non-marital couple, no children 1.451 1.182 1.78 2.55 1.839 3.534 Single, children 1.698 1.394 2.067 2.459 1.923 3.144 Single, no children 2.715 2.41 3.059 2.906 2.289 3.69 Other 1.268 1.029 1.562 1.643 1.162 2.323 Unknown 5.334 2.993 9.504 10.263 4.47 23.562 Region (ref. Flanders) Wallonia 1.002 0.915 1.098 1.058 0.908 1.232 Brussels 0.841 0.722 0.98 0.885 0.686 1.141 Area (ref. Urban) Suburban 1.091 0.984 1.21 0.892 0.75 1.061 Rural 0.991 0.885 1.111 0.925 0.766 1.117 Citizenship (ref. Belgian) Other European 0.563 0.446 0.71 0.504 0.322 0.788 Non-European 0.139 0.045 0.432 0.354 0.112 1.116 Unknown 0.741 0.104 5.275 0 . . Educational level (ref. Primary) Lower Secondary 1.094 0.938 1.276 0.967 0.753 1.242 Upper Secondary 1 0.855 1.169 1.213 0.942 1.563 Higher 0.7 0.591 0.828 1.14 0.872 1.49 Unknown 0.943 0.756 1.176 0.83 0.571 1.206 Occupational status (ref. Unemployed) Inactive 0.868 0.599 1.257 1.268 0.912 1.763 Employed 0.945 0.693 1.29 1.049 0.799 1.378 Self-employed 0.948 0.675 1.333 1.273 0.854 1.898 Unknown 1.123 0.803 1.571 0.756 0.499 1.145 Time spent in the housing (ref. Under 1 year) 1–2 years 0.832 0.705 0.983 0.865 0.645 1.161 3–5 years 0.745 0.631 0.88 0.763 0.57 1.021 5 years+ 0.719 0.621 0.833 0.778 0.602 1.007 Subjective health (ref. Very good) 1.294 1.15 1.456 1.572 1.256 1.969 Good 1.989 1.723 2.295 3.294 2.562 4.235 Intermediate 3.584 2.978 4.313 7.425 5.542 9.947 Bad 4.61 3.453 6.154 12.227 8.153 18.337 Very bad 1.54 1.095 2.167 2.314 1.362 3.933 Unknown Failures 2111 750 Observations 1398141 1365249 Log Likelihood -28903.225 -10174.052 Sensitivity analyses supported the main findings. First, models estimated without imputation of the housing and neighbourhood items yielded hazard ratios very similar to those from the imputed sample, suggesting that missing data did not materially affect the results (Table A5). Second, when restricting the sample to individuals reporting good or very good health at baseline, the protective association of high housing quality with suicide persisted for men but was attenuated and estimated with less precision for women, while associations with neighbourhood satisfaction remained small and were clearly protective only among men (Table A6). Finally, Fine–Gray subdistribution hazard models treating death from other causes and residential mobility as competing events produced estimates closely aligned with the Cox models (Tables A7-A8), indicating that informative censoring due to mortality or mobility is unlikely to explain the observed associations between living conditions and suicide. Interaction with life stages Figure 1 shows the predicted hazard of suicide according to the interaction between housing quality (divided into two groups: low and high) and age group (25–39, 40–54 and 55–69). Visually, the confidence intervals largely overlap across age groups, and a formal interaction test between housing quality and age group indicates no significant interaction (p > 0.05). The only noticeable deviation is among men aged 55–69, for whom the difference in suicide hazard between low and high housing quality is small and not clearly apparent. For women, the pattern of lower suicide hazard in high-quality housing is visible in all age groups, although estimates are imprecise, with wide confidence intervals due to the smaller number of suicides. Interaction housing-neighbourhood Figure 2 presents the interaction between housing quality (divided into two groups, “low” and “high”) and neighbourhood satisfaction (“low”, “intermediate” and “high”). For both men and women, living in low housing quality is associated with a higher suicide hazard than living in high housing quality at all levels of neighbourhood satisfaction, and the difference between low and high housing quality tends to widen as neighbourhood satisfaction increases. Although the overall interaction between housing quality and neighbourhood satisfaction is not statistically significant at the 5% level (p = 0.10), the pattern of estimates is informative. Among individuals living in good or very good housing conditions, higher neighbourhood satisfaction appears to be associated with lower predicted suicide hazards. By contrast, among those living in low or very low housing conditions, the predicted suicide hazards remain similar regardless of neighbourhood satisfaction. This pattern is evident in the point estimates but should be interpreted with caution, given the relatively wide confidence intervals, particularly for women. Discussion Interpretation of the results Among the determinants of suicide, relatively little is known about the role of the living environment in suicide mortality. To our knowledge, this is among the first population-based studies to link suicide mortality to both a detailed set of housing characteristics and neighbourhood satisfaction across multiple amenities and services. We also examined whether the association between housing quality and suicide varies across life stages and how potential mismatches between housing and neighbourhood quality relate to suicide risk. The use of linked census-register data is a key strength: the 2001 Belgian Census is a unique yet underused source on living conditions, and linkage to the National Register and death certificates enables us to follow the population at risk of suicide over five years. As a first contribution, we show that very good housing conditions are associated with 13% and 21% lower suicide hazards than poor housing conditions for men and women, respectively (H1 is supported) , after controlling for demographic and socioeconomic characteristics, self-rated health, years spent in the dwelling and municipal deprivation. This result is consistent with the principles of Learned Helplessness theory [ 18 ] and with the idea that low comfort and limited control in the living environment can contribute to psychological distress. It also resonates with previous empirical research linking poor housing quality to worse mental health [ 8 , 9 , 19 – 22 ]. In particular, Lee (2024) showed, using a broader set of functional and structural housing characteristics, that better housing was associated with better mental health outcomes and fewer suicidal ideations. Our study extends this work by documenting an association between housing quality and suicide mortality in a national cohort. Restricting the analysis to individuals who reported good or very good health at baseline, the gradient whereby better housing conditions are associated with lower suicide hazards is confirmed for men but not for women (Appendix, Table A6). This specification partly controls for a major confounding factor – physical and mental health – which is related both to suicide risk [ 11 ] and to poorer living conditions [ 59 ]. For women, the association between housing quality and suicide appears to be confounded mainly by health status, whereas for men, the association persists after accounting for self-rated health. One possible explanation is that the role of the living environment in suicide may be more direct for men, whose mental health has been shown to be strongly tied to social status and role performance [ 61 ]. Another, complementary explanation is that men may under-report their difficulties, especially regarding mental health, leading to an overestimate of their self-reported health status [ 62 , 63 ]. Second, our results suggest that the association between housing quality and suicide risk is broadly stable across the adult life course, and housing-related stressors are not specific to one age group (H1b is not validated). This pattern may reflect the universal importance of housing as a source of dignity, control and social belonging throughout adulthood. As stated in the Belgian constitution, housing is a right for everyone, and universal interventions to improve housing quality may therefore be effective across the life course. However, the mechanisms at stake may differ by age: middle-aged adults may be particularly sensitive to low housing stability and functionality [ 10 , 22 ], whereas older adults may be more affected by poor structural characteristics [ 22 ]. Future research drawing on data with repeated measures of housing quality over time could better disentangle age-, period- and cohort-related variation in these associations and clarify when in the life course housing interventions might be most beneficial. No clear association between neighbourhood satisfaction and suicide risk emerged in the full models for either men or women (H2 is not validated) , which is consistent with previous studies that found limited or no effects of neighbourhood conditions on mental health or suicidal behaviour once individual composition is taken into account [ 36 , 37 ]. In additional models restricted to men who reported good or very good health at baseline (Appendix, Table A6), a pattern appears: intermediate and high levels of neighbourhood satisfaction are associated with lower suicide hazards compared with low satisfaction. This suggests that health status may partly mask a negative relationship between neighbourhood satisfaction and suicide among men. When health is not an immediate concern, the neighbourhood context may stand out more clearly as a source of stress, low social status or frustration. Prior research indicates that women tend to benefit more from social support in relation to depressive symptoms, whereas men are more strongly affected by material conditions and status-related factors [ 61 , 64 ], which could help explain why neighbourhood satisfaction matters primarily for men in good health in our data. It is also important to note that our measure of neighbourhood quality is subjective; mental health can bias perceptions, and depression has been shown to be associated with a more pessimistic view of one’s environment [ 65 ]. Our results suggest that when individuals live in poor-quality housing, neighbourhood advantages do not buffer suicide risk (H3 is supported). In other words, a mismatch between low housing quality and higher neighbourhood satisfaction is not associated with lower suicide hazards and may even coincide with elevated risk. By contrast, higher levels of neighbourhood satisfaction appear to be associated with lower suicide risks among individuals living in good-quality housing, which points to the detrimental impact of a low-quality close environment even when the broader neighbourhood is perceived positively. For people in poor-quality housing, neighbourhood satisfaction does not alleviate the elevated suicide risk linked to poor housing. Individuals in such situations may experience relative deprivation and downward (self-)comparison, and the contrast between indoor and outdoor conditions may adversely affect mental health [ 44 , 66 ]. According to the Fundamental Cause theory [ 67 ], structural disadvantages such as poor housing may not be offset by contextual resources like better neighbourhood quality. Mechanisms of exclusion can also weaken the social ties of people whose housing conditions fall below what is locally expected [ 68 ], which is detrimental for mental health [ 69 ]. Finally, selection into discordant situations is likely: downward social mobility may lead to poor-quality housing in otherwise well-regarded neighbourhoods, and such mobility is itself a major determinant of suicide [ 70 ]. Implications Taken together, our findings underline the central role of housing conditions in suicide prevention. At the population level, even the modest relative differences we observe between housing quality groups may translate into substantial numbers of deaths averted if basic dwelling standards are improved. Policies that focus primarily on neighbourhood regeneration, aesthetics or service provision without tackling poor housing quality risk leaving the most vulnerable groups behind and may even exacerbate perceived inequalities. Our results suggest that priority should be given to ensuring access to safe, comfortable and adequately equipped housing, for example through targeted renovation programmes, minimum quality standards and support for households in the worst dwellings. Integrating housing indicators into suicide prevention strategies and mental health services – such as outreach or screening in areas with a high concentration of substandard housing – could help identify people at elevated risk. Although our data refer to early-2000s Belgium, these mechanisms are likely to remain relevant in contemporary contexts where housing precarity and energy poverty are growing concerns. These findings reinforce calls within public health to address material living conditions, and not only individual-level risk factors, in comprehensive suicide prevention strategies. Strengths and limitations Our study has several strengths. First, it relies on a high-quality, population-based dataset. The 2001 Belgian Census covers almost the entire resident population, with a very high response rate and rich information on indoor housing characteristics, satisfaction with the immediate environment and subjective health. The 2001 Census remains one of the most detailed and underused sources on housing and neighbourhood quality in Belgium, even though it now refers to an earlier period. We used simple group-based imputations for the housing and neighbourhood scores to reduce selection bias due to missing data, and robustness checks suggest that this did not materially affect the results. A second strength is the construction of two composite scores for housing and neighbourhood quality based on principal component analysis and grouped into halves, thirds or quartiles. This approach is transparent and easily replicable, encouraging researchers in other countries with suitable data to reproduce and extend our analyses. The Belgian context offers an additional advantage for interpretation. Belgium has long had suicide rates above the European average, while housing conditions have been comparatively good since the 2000s, with a proportion of overcrowded homes well below the European average [ 71 ] and a relatively high rate of homeownership [ 72 ], often associated with better housing quality. This means our study is conducted in a setting where extremely poor housing conditions and very pronounced housing inequalities are relatively rare, and where debates about a housing “crisis” have sometimes been considered exaggerated [ 73 ]. Future work could replicate this analysis in contexts with more marked housing inequalities to examine whether greater prevalence and intensity of poor living environments further increase suicide risk among the most vulnerable, or whether widespread disadvantage changes the role of relative deprivation and social stigma. Our study also has limitations. First, it cannot establish a causal relationship between environmental conditions and suicide mortality. We observe living conditions at a single point in time, through the 2001 Census, and do not have information on housing and neighbourhood trajectories over the life course. With repeated measures of housing quality and neighbourhood satisfaction, it would be possible to better assess temporality and strengthen causal claims. We partially address this issue by re-estimating the models in the subsample of individuals who reported good or very good subjective health at baseline (Appendix, Table A6). This helps to control for a major confounding factor – poor physical and mental health – which is strongly associated with both housing conditions and suicide risk. The comparison between the full and restricted samples highlights where associations are largely confounded by predisposition to poor health. Second, mortality from other causes and residential mobility may not be neutral censoring events. In the main Cox models, we assume that death from other causes (including causes related to substance use) and residential mobility are independent of suicide risk. Yet our findings show that short residence duration (less than one year) is associated with higher suicide risk than longer residence, suggesting that residential change and suicide may be related. We therefore estimated Fine-Gray subdistribution hazard models, treating death from other causes and residential mobility as competing events rather than censoring (Appendix, Tables A7-A8). These models yield results very similar to the Cox models for the associations between living conditions and suicide, suggesting that informative censoring due to mortality or mobility is unlikely to bias the estimated relationship between housing conditions and suicide risk. Conclusion We contribute to the existing literature by quantifying the association between housing quality and suicide, independently of education, occupation, marital status and other individual characteristics. In early-2000s Belgium, this association is clear across the working-age population, underscoring the importance of policies that guarantee access to adequate housing, including essential facilities, energy-efficient renovations and maintenance of basic installations. Our results highlight the primacy of the home environment in relation to suicide risk: improvements in broader neighbourhood conditions appear to offer protective effects only when basic housing adequacy is first ensured. These findings add to a growing body of work calling for integrated housing and mental health policies, with particular attention to the most deprived housing sectors. Declarations Ethics approval and consent to participate The study is based on pseudonymised census and register data provided by Statistics Belgium. The use of these data for scientific research was approved by Statistics Belgium ethical board. In line with Belgian regulations on secondary use of administrative data, individual informed consent was not required. Consent for publication Not applicable Availability of data and materials. The micro-level census and register data analysed during the current study are not publicly available due to legal restrictions on data confidentiality. Access can be requested from Statistics Belgium under strict conditions for accredited researchers. The authors are not permitted to share the data directly. Competing interest. The author declares no competing interest. Funding The author has received an FNRS Fellow researcher mandate to conduct this research. Contribution JD conducted and coordinated the whole study, which includes the selection of the design, the data preparation and analysis, the visualization and interpretation of the results and the manuscript writing. Acknowledgment The study uses data from Statbel (Directorate-general Statistics – Statistics Belgium) – Demobel (adaptation of the National Register). Computational resources have been provided by the supercomputing facilities of the Université Catholique de Louvain (CISM/UCL) and the Consortium des Equipements de Calcul Intensif en Fédération Wallonie-Bruxelles (CECI) funded by the Fond de la Recherche Scientifique de Belgique (FRS-FNRS) under convention 2.5020.11 and by the Walloon Region. The author thanks Statistics Belgium and, particularly, Patrick Lusyne and Cloë Ost for preparing and anonymising the dataset. Open access funded by the Library of the University of Helsinki. 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Housing in Europe. 2022 [cited 2025 July 11]. https://ec.europa.eu/eurostat/cache/digpub/housing/bloc-1a.html . Accessed 11 July 2025. Winters S. A Housing Crisis in Belgium: Perception or Reality? In: Egner B, Krapp M-C, editors. Housing in Crisis: Policies and Challenges in Europe [Internet]. Cham: Springer Nature Switzerland; 2025. pp. 13–30. [cited 2025 July 9]. https://doi.org/10.1007/978-3-031-87267-9_2 . Additional Declarations No competing interests reported. Supplementary Files Appendices2025.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-8338834","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":575242213,"identity":"75fbd292-a97f-4945-b4e0-ecbd8b83ce67","order_by":0,"name":"Joan Damiens","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYBACxgYQyQZGDAwfGxhkGJiBMIFBjocoLYwzGxh4oFqMcWqBADYIxcwL0gKkgdgYp2Lm9uZnDxjK7KL52HsPf7bdYcMj38572OABg4EMTof1HDM3YDiXnNvGcy5NOvdMGo/BYb7khAQGA9x+mZFgJsHYxpzbJpFjBiQP8xgw8xgfSGD4g0dL+jeglvrcNvk3xp8t2/7zyDeDteCzJQdky2GgLTwG0oxtB3gYDvMY43dYz5kyiYRzx4F+yTGT7G1LBvqFx9ggwQC3FsP29m0SH8qqc+e3nzH+8LPNTk6+/4yx5I8KA3ucWhqARAKmuAEuDQwM8rilRsEoGAWjYBRAAQAsqkpqmYVGegAAAABJRU5ErkJggg==","orcid":"","institution":"University of Helsinki","correspondingAuthor":true,"prefix":"","firstName":"Joan","middleName":"","lastName":"Damiens","suffix":""}],"badges":[],"createdAt":"2025-12-11 16:38:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8338834/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8338834/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100539455,"identity":"81bea1cd-44ec-48c2-b778-e4e9cb82fc62","added_by":"auto","created_at":"2026-01-19 05:04:04","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129533,"visible":true,"origin":"","legend":"","description":"","filename":"HQSmanuscript2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-8338834/v1/4e61c04ddf6fc96302f0e300.docx"},{"id":100539448,"identity":"03b191ba-1539-4fd4-950a-a9e1f53038a3","added_by":"auto","created_at":"2026-01-19 05:04:00","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5624,"visible":true,"origin":"","legend":"","description":"","filename":"18c7fbcae9d34c2496c5e8104605c633.json","url":"https://assets-eu.researchsquare.com/files/rs-8338834/v1/e4857ea23394c0aa906a31a7.json"},{"id":100539449,"identity":"340eb6ed-8d7a-473d-8e74-71efcdb4fbe7","added_by":"auto","created_at":"2026-01-19 05:04:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93138,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-8338834/v1/a680487e7367834b695d3d9b.docx"},{"id":100539452,"identity":"e6781486-691e-4a89-8441-e02bbb081417","added_by":"auto","created_at":"2026-01-19 05:04:01","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":227360,"visible":true,"origin":"","legend":"","description":"","filename":"18c7fbcae9d34c2496c5e8104605c6331enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8338834/v1/458f2566756de996b11d9c0e.xml"},{"id":100539450,"identity":"c9477935-e2aa-4653-92cb-144b09584bae","added_by":"auto","created_at":"2026-01-19 05:04:00","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":224511,"visible":true,"origin":"","legend":"","description":"","filename":"18c7fbcae9d34c2496c5e8104605c6331structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8338834/v1/edbf7de24e07640d461f62f6.xml"},{"id":100539453,"identity":"c9d5093c-4164-413b-a791-c743ef026e3f","added_by":"auto","created_at":"2026-01-19 05:04:03","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":244232,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8338834/v1/d45b12d0d136746a46b89e01.html"},{"id":100539447,"identity":"dfb0ccf5-abc8-49c8-8c80-4c5a9228c5ce","added_by":"auto","created_at":"2026-01-19 05:04:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66395,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted hazard of suicide for men and women according to their age group for each of the housing quality halves (low, high). Based on the previous model.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8338834/v1/b7b7511cbdd4c52bcaa03150.jpg"},{"id":100539451,"identity":"fc6d82b9-168d-426a-994d-c073666c0a4b","added_by":"auto","created_at":"2026-01-19 05:04:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73499,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted relative hazard of suicide for men and women according to neighbourhood satisfaction level (low, intermediate, high) for each of the housing quality halves (low, high). Based on the previous model.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8338834/v1/fb9df5cbb99a95733234118d.jpg"},{"id":102297721,"identity":"72838c22-85e4-4a28-8f6b-eb1e3daff784","added_by":"auto","created_at":"2026-02-10 10:28:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1912932,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8338834/v1/16276f72-e4de-4428-b392-ec8065cc2c61.pdf"},{"id":100539454,"identity":"8ae84023-5bd2-48a2-bafe-f093b89c3113","added_by":"auto","created_at":"2026-01-19 05:04:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":93138,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-8338834/v1/33cca88aee282a30c737d03a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Housing Conditions, Neighbourhood Satisfaction and Suicide Mortality in Belgium: A Population-Based Cohort Study","fulltext":[{"header":"Contributions to the literature","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003eUses population-wide linked census and mortality data to quantify how detailed housing conditions are associated with suicide mortality, beyond individual risk factors.\u003c/li\u003e\n \u003cli\u003eShows that neighbourhood advantages benefit suicide risk primarily among people living in good-quality housing, while neighbourhood satisfaction does not offset the elevated suicide risk associated with poor housing conditions.\u003c/li\u003e\n \u003cli\u003eHighlights housing quality as a key determinant for suicide prevention and supports integrated housing and mental health policies that prioritise the most deprived housing sectors.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Methods","content":"\u003cp\u003eData sources and study population\u003c/p\u003e\u003cp\u003eOur main data source is the 2001 Belgian socioeconomic survey (2001 Belgian Census). Self-administered questionnaires were sent in autumn 2001 to the entire population living in Belgium (10,296,349 individuals), and it is estimated that 3.1% did not respond even after at least three reminders [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Although these data refer to the early 2000s, the 2001 Census remains one of the most detailed and underused sources of information on living conditions in Belgium. It provides population-level information on housing characteristics, satisfaction with the immediate environment and subjective health. One reference person in each household answered the questions on housing and neighbourhood conditions. The Appendix (Tables A1-A2) presents the housing- and neighbourhood-related items.\u003c/p\u003e\u003cp\u003eThe 2001 Census was linked to the National Register and death certificates from 1 January 2002 to 31 December 2006, by Statistics Belgium (pseudo-anonymised data). Death certificates provide information on the date of death and the underlying, intermediate and immediate causes of death, coded using the 10th International Classification of Diseases (ICD-10).\u003c/p\u003e\u003cp\u003eWe excluded people living in collective households (care facilities, hospitals, monasteries, military compounds, prisons, etc.), who represented about 1% of the population in 2001, and restricted the analysis to adults aged 25–69 years. Mental health problems in younger and older populations often follow distinct trajectories: among younger individuals, onset of conditions such as schizophrenia, bipolar disorder or eating disorders – which are linked to suicide risk – is more common [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], whereas older adults are more affected by declining physical health, loss of mobility, chronic pain and reduced social contact [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In these age groups, housing conditions may play a less direct role in mental health, as they often reflect other underlying circumstances, such as parental socioeconomic status for adolescents and young adults, or the health status of elderly individuals.\u003c/p\u003e\u003cp\u003eMeasures\u003c/p\u003e\u003cp\u003eSuicide\u003c/p\u003e\u003cp\u003eSuicide includes all deaths due to intentional self-harm, i.e. deaths coded X60-X84 and Y87.0 in ICD-10, whether this cause is recorded as underlying, intermediate or immediate. Between 2002 and 2006, among individuals aged 25–69 in 2001, we observe 88,470 deaths for men, including 5,158 suicides, and 48,903 deaths for women, including 2,030 suicides.\u003c/p\u003e\u003cp\u003eHousing- and neighbourhood-related indicators\u003c/p\u003e\u003cp\u003eWe constructed housing and neighbourhood quality scores from multiple indicators using principal component analysis (PCA). Following a previous study [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], we retained the first component with the highest eigenvalue and defined this as the housing or neighbourhood quality score. The Appendix (Tables A1-A2) lists all items, and Tables A3-A4 report the PCA results.\u003c/p\u003e\u003cp\u003eThe Census provides information on general and basic dwelling characteristics and the quality of installations. Based on these indicators, we derived a housing quality score for each household and assigned it to all its members. The score was then divided into quartiles (very low, low, high, very high) and, for interaction analyses, further collapsed into two groups at the median (low and high). Because the distribution is skewed towards better scores, these two groups can be interpreted as low-intermediate (low) and high-excellent (high) housing quality. The scoring procedure gives higher weights to basic facilities (bathroom, central heating) and indoor items (quality of inside walls, electrical system, windows and presence of double glazing) than to outdoor items (quality of outside walls, pipes and roof). Lower weights are assigned to more general housing characteristics (type of building, presence of a garden, overcrowding).\u003c/p\u003e\u003cp\u003eNeighbourhood-related indicators capture the general characteristics of the surroundings and the availability and quality of local services. We constructed a neighbourhood quality score from these items and divided it into thirds of the population (low, intermediate, high). Higher weights were given to more subjective elements (aesthetics, cleanliness, quietness), whereas service-related items contributed less to the overall score.\u003c/p\u003e\u003cp\u003eCovariates\u003c/p\u003e\u003cp\u003eThe models adjust for demographic and socioeconomic covariates. Suicide risk varies strongly with age (Appendix, Figure A1). We also account for marital and parental status, measured through household composition, distinguishing unpartnered individuals from those in marital and non-marital unions, with and without children in the household. Non-marital unions are defined as households with exactly two unrelated opposite-sex adults with an age difference of less than 15 years, following Belgian demographic research [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. We further adjust for citizenship (Belgian, other European, non-European) and urbanicity of the municipality of residence (urban, suburban, rural), which are associated with suicide [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Belgian regions (Flanders, Wallonia, Brussels) differ in housing policies and cultural characteristics, including higher religiosity in Flanders, and are therefore included as covariates.\u003c/p\u003e\u003cp\u003eSocioeconomic covariates include educational attainment (primary, lower secondary, upper secondary, higher/tertiary) and occupational status (unemployed, inactive, employed, self-employed). We add the number of years spent in the current dwelling as an approximation of residential attachment and satisfaction [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Finally, we include the Belgian Index of Multiple Deprivation (BIMD) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], a multidimensional measure of municipal socioeconomic conditions based on employment, education, income and crime (excluding the housing component), constructed from the 2001 Census and categorised into deciles of municipalities.\u003c/p\u003e\u003cp\u003eA word on missing values\u003c/p\u003e\u003cp\u003eIn the 2001 Census, 5.2% of respondents did not answer any of the questions on housing or neighbourhood conditions, and 14.9% did not respond to at least half of them. Non-respondents in the Census have a higher mortality risk, comparable to that of the most underprivileged groups (Bourguignon et al., 2022), and a higher risk of suicide in 2002 (46.4 per 100,000 among non-respondents vs. 24.3 per 100,000 in the general population). To limit data loss while reducing bias, we first allowed individuals to have at most three missing items per score. For those with up to three missing items, we used group-mean imputation. For the housing quality score, we assigned the mean score of individuals with the same educational level, occupational status, household composition, nationality and region of residence. For the neighbourhood quality score, we assigned the mean score of the statistical sector – the smallest geographical unit in Belgium, with an average of around 500 inhabitants. Individuals with more extensive missingness were excluded from the analyses.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eMain analysis\u003c/p\u003e\u003cp\u003eWe first describe associations between suicide rates, housing and neighbourhood scores, and independent variables using analyses of variance (ANOVA). These tests are used purely descriptively to compare crude rates and mean scores across groups.\u003c/p\u003e\u003cp\u003eTo control for potential confounders, we estimate Cox proportional hazards models to assess the risk of suicide among adults aged 25–69 years living in Belgium between 2002 and 2006. The hazard function is specified as\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\begin{array}{c}h\\left(t,X\\right)={h}_{0}\\left(t\\right)\\:exp\\left(\\sum\\:_{i=1}^{p}{\\beta\\:}_{i}{X}_{i}\\right)\\#\\left(1\\right)\\end{array}\\)\u003c/span\u003e \u003c/span\u003ehere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{0}\\left(t\\right)\\:\\)\u003c/span\u003e\u003c/span\u003e is the baseline hazard at the beginning of the observation, \u003cem\u003eX\u003c/em\u003e is the vector of the \u003cem\u003ep\u003c/em\u003e covariates of the model and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{\\:}\\)\u003c/span\u003e\u003c/span\u003eare the regression coefficients. The time scale is time since baseline (1 January 2002). All individuals alive and resident in Belgium on 1 January 2002 enter the risk set. Follow-up runs to 31 December 2006. Individuals are right-censored at the time of death from other causes, emigration, or residential moves within Belgium, because the housing information collected in 2001 no longer reflects their current dwelling. We assessed the proportional hazards assumption for all covariates and found no evidence of violation.\u003c/p\u003e\u003cp\u003eTo investigate interactions between housing quality and neighbourhood satisfaction by age group, we compute predicted hazard ratios of suicide based on the Cox models. These are independent of the baseline hazard and are given by\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}\\widehat{hr}=\\:\\frac{\\widehat{h\\left(t,X\\right)}}{{h}_{0}\\left(t\\right)}=\\text{exp}\\widehat{\\beta\\:}X\\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eBecause we analyse population data rather than a sample, the use of inferential statistics warrants caution [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. We nevertheless report 95% confidence intervals as measures of precision, keeping in mind that the rarity of the outcome may lead to wide intervals. In interpreting results, we consider both statistical significance (confidence intervals) and the magnitude of differences in estimates between categories (substantive significance) [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRobustness checks\u003c/p\u003e\u003cp\u003eModels estimated without imputation of the housing and neighbourhood scores are presented in the Appendix (Table A5) and yield results similar to those of the main analyses. To address a major confounder, predisposition to poor physical and mental health – which is strongly associated with both housing careers [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] and suicide mortality [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] – we re-estimate the models in the subsample of individuals who reported good or very good health at baseline (Appendix, Table A6). This subsample is less likely to include individuals with severe health limitations at the start of follow-up. Differences in results between the main and restricted samples are discussed in the Discussion section.\u003c/p\u003e\u003cp\u003eWe additionally estimate Fine-Gray subdistribution hazard models [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], treating death from other causes and residential mobility as competing events (Appendix, Tables A7–A8). Instead of censoring individuals who die from other causes or move, these models account for the possibility that such events are alternative outcomes to suicide that could bias our estimates if ignored. The subdistribution hazard ratios obtained from these models are closely aligned with the Cox estimates, suggesting that informative censoring due to mortality or mobility is unlikely to explain the observed associations.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eSince the 1990s, suicide rates in Europe have decreased, but they remain high in Belgium. In 2021, Belgium recorded 15.4 suicides per 100,000 inhabitants (19.4 in 2005), compared with 10.2 per 100,000 in the European Union (13.3 in 2005) [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Extensive research has examined determinants of suicide such as education [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and marital status [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In contrast, the role of the living environment – defined here by housing and neighbourhood quality – in suicide mortality remains poorly understood and has rarely been quantified using population-based data.\u003c/p\u003e \u003cp\u003eHousing conditions are recognised as important determinants of physical health and longevity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and their relationship with mental health and wellbeing has been documented [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], often using a limited set of indicators such as household density or building type. While this work shows that the home environment matters for mental health, much less is known about how housing and neighbourhood quality relate to suicide. Previous studies have also suggested that the association between housing tenure and suicide varies over the life course, being strongest in mid-life when pressures to achieve homeownership are highest [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Testing whether the association between living environment quality and suicide differs across life stages may therefore help to clarify the mechanisms linking housing to suicidal behaviour, for instance, if pressures to secure good-quality housing cumulate with pressures to become a homeowner. Finally, existing literature has not yet investigated how relative inequalities in living conditions, and in particular discordance between housing and neighbourhood quality – living in low-quality housing in an otherwise good neighbourhood, or vice versa – might affect suicide risk.\u003c/p\u003e \u003cp\u003eIn this article, we use linked census-register data covering the entire Belgian population aged 25–69 years to (1) quantify the association between housing conditions and suicide mortality; (2) test whether this association varies across adult life stages; and (3) examine how concordance and discordance between housing quality and neighbourhood satisfaction relate to suicide risk. By focusing on suicide mortality rather than self-reported mental health outcomes, and by jointly modelling housing and neighbourhood conditions, we contribute to population research on mortality, population health and human-environment interactions.\u003c/p\u003e \u003cp\u003eHousing and mental health\u003c/p\u003e \u003cp\u003eThe most well-established suicide determinants are depression and previous suicidal attempts [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Housing stability [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and tenure [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] have also been linked to suicide, with tenants showing higher risks than owners. Beyond this aspect of residential stability, however, much less is known about how characteristics of the close living environment relate to suicide mortality. In contrast, a substantial literature has examined how housing and neighbourhood conditions can shape other mental health outcomes such as life satisfaction, depression and suicidal ideation. Given the strong links between life satisfaction, mental health and suicide mortality [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], we use this work to define our hypotheses. Life satisfaction – one’s appraisal of their life – can protect against suicidal thoughts and behaviours by supporting feelings of belonging, hope and trust in the future [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Depression and other mental health problems may mediate the pathway from low life satisfaction to suicidal behaviour by fostering a sense of failure and negative self-perception [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Among the determinants of life satisfaction, the living environment is central, as it is an immediate reflection of one’s socioeconomic achievements and material comfort.\u003c/p\u003e \u003cp\u003eSince the 1990s, the Belgian constitution has recognised access to decent housing as a fundamental right and a cornerstone of human dignity. Previous studies show that poor housing characteristics, such as dwelling type, age, floor level, small surface area and low overall comfort, are associated with worse mental health and wellbeing [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Living in a high-rise building [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] or in overcrowded dwellings [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] are associated with a higher risk of depressive symptoms and anxiety. Drawing on Learned Helplessness theory, low control over one’s environment and persistent discomfort are likely to increase risks of depression and psychological distress [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], especially for low-income households who may feel trapped in unsatisfactory housing because moving is difficult, costly and demanding. Quantitative studies often rely on a limited set of housing indicators and adjust insufficiently for socioeconomic characteristics, whereas qualitative work highlights a broader range of conditions – noise, damp, and inability to keep the home warm – that are associated with lower life satisfaction and wellbeing [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e–\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. One South Korean population-based panel study is particularly notable for using a more extensive set of housing variables [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. It shows that functional issues (ventilation, noise, heating, lighting) increase depression and suicidal ideation among middle-aged adults, while structural problems (building permanence, material quality, resistance to heat, fire and moisture) are more strongly related to depression in older adults. Residential satisfaction partly mediates the association with suicidal ideation, especially for young men. Because depression and severe psychological distress are major risk factors for suicidal behaviour, it is plausible that better housing quality also protects against suicide risk. \u003cb\u003eWe therefore hypothesise that higher housing quality is associated with a lower risk of suicide for both men and women, after accounting for demographic and socioeconomic characteristics (H1).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDifferences across life stages and gender\u003c/p\u003e \u003cp\u003ePrevious research suggests that the relationship between housing conditions and general health may intensify with age [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and that associations between living environments and mental health can vary across the life course. A British study, for example, found that the relationship between green space and well-being changed with age, decreasing for men and increasing for women [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For suicidal thoughts and behaviours, evidence points to a somewhat different pattern. In South Korea, housing problems related to ventilation, noise, heating or lighting increased the likelihood of suicidal ideation mainly among middle-aged adults [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Similarly, Belgian research showed that the association between homeownership and suicide was most pronounced in mid-life [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Middle adulthood is a period when worry, dissatisfaction, job demands and insecurity are often at their highest [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], while the desire for stability peaks [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A mismatch between expectations and realities – regarding housing conditions, for instance – may undermine feelings of belonging and trust in the future [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], two key determinants of suicidal behaviour [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In contrast, younger adults may have more flexible housing pathways [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and older adults may show greater resilience to adversity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. \u003cb\u003eWe therefore hypothesise that the protective association of housing quality with suicide risk differs across adult life stages and is strongest in mid-life (around ages 40–54) (H1b).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNeighbourhood quality and mental health\u003c/p\u003e \u003cp\u003eAt the neighbourhood level, the presence of green spaces is associated with better mental health [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] as are higher perceived cleanliness [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and better air quality [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, the literature is not univocal: some papers find no clear association between the neighbourhood conditions and mental health [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] or suicidal behaviour once individual composition is taken into account [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The relationship between access to local services and mental health has been less studied, but existing work suggests that a better perception of service provision is associated with better mental health, particularly in more deprived communities [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and that inequalities in the distribution of mental healthcare providers and other area resources are related to unmet mental healthcare needs and suicide mortality [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Because poor mental health is a major risk factor for suicidal behaviour, it is plausible that living in a neighbourhood that is perceived as clean, safe, well-served and generally satisfactory also reduces suicide risk. \u003cb\u003eWe therefore hypothesise that higher neighbourhood satisfaction is associated with a lower risk of suicide, net of individual demographic and socioeconomic characteristics and housing quality (H2).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDiscordance between housing and neighbourhood quality and mental health\u003c/p\u003e \u003cp\u003eThe interaction between housing and neighbourhood conditions represents an important yet still understudied dimension of suicide risk. A study conducted in eight European cities found that, in low-quality neighbourhoods, the association between housing quality and psychological well-being was stronger than in higher-quality neighbourhoods, suggesting an accumulation of detrimental elements in the close environment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. However, other work has not shown that better neighbourhood quality can offset the negative association between poor housing and low well-being [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. According to the compound disadvantage hypothesis, the most vulnerable groups – such as those in poor housing – are likely to suffer most from adverse neighbourhood conditions [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and lower-income households are also more dependent on local neighbourhood resources and less able to draw on opportunities outside their residential area [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Yet living in a high-quality neighbourhood is not necessarily beneficial for those in poor-quality housing, especially when their circumstances are discordant with those of their neighbours. Relative deprivation theory posits that individuals evaluate their situation relative to those around them [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Individuals living in substandard housing within otherwise affluent or high-quality neighbourhoods may experience heightened feelings of relative deprivation, stigma and social exclusion [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], which are linked to suicidal ideation and behaviour. Evidence that people report lower life satisfaction when neighbours’ incomes surpass their own, even after accounting for absolute income [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], supports the broader role of relative position in shaping mental health. \u003cb\u003eWe therefore hypothesise that the association between neighbourhood satisfaction and suicide depends on housing quality: among people living in good-quality housing, higher neighbourhood satisfaction is associated with lower suicide risk, whereas among those living in poor-quality housing, neighbourhood advantages do not reduce suicide risk (H3).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBy analysing suicide mortality in relation to housing and neighbourhood conditions in a whole-population cohort, this study contributes to understanding the structural determinants of suicide and provides evidence relevant for housing and mental health policy in Belgium and comparable welfare states.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the crude suicide rates of the population according to the characteristics that will be included in the models. People living in high and, especially, very high housing quality present lower suicide rates than those living in low and very low housing quality. The population that is more satisfied with their neighbourhood also shows lower suicide rates. Suicide rates are lower among higher-educated groups. People in marital unions, as well as parents, present lower suicide rates compared to unpartnered individuals or persons in non-marital unions. People living in Brussels, homeowners and non-Belgians show lower suicide rates. When it comes to areas of residence (urban, suburban, rural), no clear differences in suicide rates are observed. Suicide rates are higher for individuals with poorer self-reported health, and longer residence duration in the current dwelling is associated with lower suicide risk.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e also displays the means of the housing and neighbourhood quality scores. On average, higher educational attainment and more advantaged occupational statuses are associated with better housing conditions. People who graduated at most from lower secondary school and unemployed people present the lowest levels of neighbourhood satisfaction, while inactive populations are the most satisfied with their neighbourhood. Individuals in marital unions tend to have higher housing and neighbourhood quality, whereas one-person households and lone parents more often live in worse conditions. Living in Wallonia is associated with a lower mean housing score, while Flemish residents report the lowest neighbourhood satisfaction. Urban residents present the lowest means for both scores. Owners live in better quality housing and neighbourhoods than tenants, and Belgians have higher average housing and neighbourhood scores than non-Belgians. Poor self-reported health and shorter residence duration are also associated with worse housing and neighbourhood conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSuicide rates, mean of housing quality score and mean of neighbourhood satisfaction score of the 25-69-year-old population living in Belgium, in the 2002\u0026ndash;2006 period, according to individual characteristics, with results of ANOVA tests between suicide rates or means of scores and predictors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSuicide rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eHousing quality score mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eNeighbourhood satisfaction score mean\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003estd err\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eANOVA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003estd err\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eANOVA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStd error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eANOVA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousing quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery low housing quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeighbourhood satisfaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow neighbourhood satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower Secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher Secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eActivity status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold composition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried with children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried without children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried with children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried without children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLone-parent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle without child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlanders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWallonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrussels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousing tenure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTenant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrbanicity of municipality of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuburban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCitizenship\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelgian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther European\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-European\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubjective health level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBad health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery bad health status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of years spent in the dwelling\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than a year in the housing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5 years in the housing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote for the ANOVA test: *: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **: p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ****: p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of the Cox proportional hazards models for suicide risk. Better housing quality is associated with lower suicide hazards for both men and women. For men, compared with the 25% of the population living in the worst housing conditions (first quartile of the housing score), living in very high housing quality (highest quartile) is associated with a 12.6% lower suicide hazard. For women, those living in very good housing conditions have a 20.9% lower suicide hazard than those in the poorest housing conditions. By contrast, for neighbourhood satisfaction, the estimated hazard ratios are close to 1 and the 95% confidence intervals include 1, so we do not find clear evidence of an association between the neighbourhood quality score and suicide risk.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Cox proportional hazard model of the risk of suicide in 2002\u0026ndash;2006 expressed in Hazard ratios (HR) and 95% confidence intervals.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIC95%\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eIC95%\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHousing quality (ref. Very low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow housing quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.828\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.04\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.823\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.961\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh housing quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.79\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.006\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.523\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.974\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery high (Q4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.765\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.998\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.633\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.987\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNeighbourhood quality score (ref. Low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.858\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.087\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.84\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.24\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.809\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.157\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.722\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.32\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.979\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.988\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.969\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.985\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousing tenure (ref. Owner)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTenant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.219\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.527\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e1.164\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.691\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.025\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.606\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.569\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e3.325\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHousehold composition (ref. Marital couple, children)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital couple, no children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.731\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.965\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e1.106\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.726\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-marital couple, children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.025\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.594\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.417\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.242\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-marital couple, no children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.182\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.78\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e1.839\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e3.534\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle, children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.394\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.067\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e1.923\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e3.144\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle, no children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2.41\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e3.059\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e2.289\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e3.69\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.029\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.562\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e1.162\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e2.323\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2.993\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e9.504\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e4.47\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e23.562\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion (ref. Flanders)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWallonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.915\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.098\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.908\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.232\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrussels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.722\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.98\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.686\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.141\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea (ref. Urban)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuburban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.984\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.21\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.75\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.061\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.885\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.111\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.766\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.117\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitizenship (ref. Belgian)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther European\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.446\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.71\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.322\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.788\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-European\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.045\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.432\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.112\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.116\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.104\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e5.275\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level (ref. Primary)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower Secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.938\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.276\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.753\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.242\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.855\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.169\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.942\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.563\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.591\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.828\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.872\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.49\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.756\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.176\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.571\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.206\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOccupational status (ref. Unemployed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.599\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.257\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.912\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.763\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.693\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.29\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.799\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.378\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.675\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.333\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.854\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.898\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.803\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.571\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.499\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.145\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTime spent in the housing (ref. Under 1 year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.705\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.983\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.645\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.161\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.631\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.88\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.57\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.021\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 years+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.621\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.833\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.602\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.007\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective health (ref. Very good)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.15\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.456\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e1.256\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.969\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.723\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.295\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e2.562\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e4.235\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2.978\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e4.313\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e5.542\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e9.947\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3.453\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e6.154\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e8.153\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e18.337\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery bad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.095\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.167\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e1.362\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e3.933\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFailures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1398141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1365249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-28903.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10174.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSensitivity analyses supported the main findings. First, models estimated without imputation of the housing and neighbourhood items yielded hazard ratios very similar to those from the imputed sample, suggesting that missing data did not materially affect the results (Table A5). Second, when restricting the sample to individuals reporting good or very good health at baseline, the protective association of high housing quality with suicide persisted for men but was attenuated and estimated with less precision for women, while associations with neighbourhood satisfaction remained small and were clearly protective only among men (Table A6). Finally, Fine\u0026ndash;Gray subdistribution hazard models treating death from other causes and residential mobility as competing events produced estimates closely aligned with the Cox models (Tables A7-A8), indicating that informative censoring due to mortality or mobility is unlikely to explain the observed associations between living conditions and suicide.\u003c/p\u003e \u003cp\u003eInteraction with life stages\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the predicted hazard of suicide according to the interaction between housing quality (divided into two groups: low and high) and age group (25\u0026ndash;39, 40\u0026ndash;54 and 55\u0026ndash;69). Visually, the confidence intervals largely overlap across age groups, and a formal interaction test between housing quality and age group indicates no significant interaction (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The only noticeable deviation is among men aged 55\u0026ndash;69, for whom the difference in suicide hazard between low and high housing quality is small and not clearly apparent. For women, the pattern of lower suicide hazard in high-quality housing is visible in all age groups, although estimates are imprecise, with wide confidence intervals due to the smaller number of suicides.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInteraction housing-neighbourhood\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the interaction between housing quality (divided into two groups, \u0026ldquo;low\u0026rdquo; and \u0026ldquo;high\u0026rdquo;) and neighbourhood satisfaction (\u0026ldquo;low\u0026rdquo;, \u0026ldquo;intermediate\u0026rdquo; and \u0026ldquo;high\u0026rdquo;). For both men and women, living in low housing quality is associated with a higher suicide hazard than living in high housing quality at all levels of neighbourhood satisfaction, and the difference between low and high housing quality tends to widen as neighbourhood satisfaction increases. Although the overall interaction between housing quality and neighbourhood satisfaction is not statistically significant at the 5% level (p\u0026thinsp;=\u0026thinsp;0.10), the pattern of estimates is informative. Among individuals living in good or very good housing conditions, higher neighbourhood satisfaction appears to be associated with lower predicted suicide hazards. By contrast, among those living in low or very low housing conditions, the predicted suicide hazards remain similar regardless of neighbourhood satisfaction. This pattern is evident in the point estimates but should be interpreted with caution, given the relatively wide confidence intervals, particularly for women.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eInterpretation of the results\u003c/p\u003e \u003cp\u003eAmong the determinants of suicide, relatively little is known about the role of the living environment in suicide mortality. To our knowledge, this is among the first population-based studies to link suicide mortality to both a detailed set of housing characteristics and neighbourhood satisfaction across multiple amenities and services. We also examined whether the association between housing quality and suicide varies across life stages and how potential mismatches between housing and neighbourhood quality relate to suicide risk. The use of linked census-register data is a key strength: the 2001 Belgian Census is a unique yet underused source on living conditions, and linkage to the National Register and death certificates enables us to follow the population at risk of suicide over five years.\u003c/p\u003e \u003cp\u003eAs a first contribution, we show that very good housing conditions are associated with 13% and 21% lower suicide hazards than poor housing conditions for men and women, respectively \u003cb\u003e(H1 is supported)\u003c/b\u003e, after controlling for demographic and socioeconomic characteristics, self-rated health, years spent in the dwelling and municipal deprivation. This result is consistent with the principles of Learned Helplessness theory [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and with the idea that low comfort and limited control in the living environment can contribute to psychological distress. It also resonates with previous empirical research linking poor housing quality to worse mental health [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In particular, Lee (2024) showed, using a broader set of functional and structural housing characteristics, that better housing was associated with better mental health outcomes and fewer suicidal ideations. Our study extends this work by documenting an association between housing quality and suicide mortality in a national cohort.\u003c/p\u003e \u003cp\u003eRestricting the analysis to individuals who reported good or very good health at baseline, the gradient whereby better housing conditions are associated with lower suicide hazards is confirmed for men but not for women (Appendix, Table A6). This specification partly controls for a major confounding factor \u0026ndash; physical and mental health \u0026ndash; which is related both to suicide risk [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and to poorer living conditions [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. For women, the association between housing quality and suicide appears to be confounded mainly by health status, whereas for men, the association persists after accounting for self-rated health. One possible explanation is that the role of the living environment in suicide may be more direct for men, whose mental health has been shown to be strongly tied to social status and role performance [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Another, complementary explanation is that men may under-report their difficulties, especially regarding mental health, leading to an overestimate of their self-reported health status [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecond, our results suggest that the association between housing quality and suicide risk is broadly stable across the adult life course, and housing-related stressors are not specific to one age group \u003cb\u003e(H1b is not validated).\u003c/b\u003e This pattern may reflect the universal importance of housing as a source of dignity, control and social belonging throughout adulthood. As stated in the Belgian constitution, housing is a right for everyone, and universal interventions to improve housing quality may therefore be effective across the life course. However, the mechanisms at stake may differ by age: middle-aged adults may be particularly sensitive to low housing stability and functionality [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], whereas older adults may be more affected by poor structural characteristics [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Future research drawing on data with repeated measures of housing quality over time could better disentangle age-, period- and cohort-related variation in these associations and clarify when in the life course housing interventions might be most beneficial.\u003c/p\u003e \u003cp\u003eNo clear association between neighbourhood satisfaction and suicide risk emerged in the full models for either men or women \u003cb\u003e(H2 is not validated)\u003c/b\u003e, which is consistent with previous studies that found limited or no effects of neighbourhood conditions on mental health or suicidal behaviour once individual composition is taken into account [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In additional models restricted to men who reported good or very good health at baseline (Appendix, Table A6), a pattern appears: intermediate and high levels of neighbourhood satisfaction are associated with lower suicide hazards compared with low satisfaction. This suggests that health status may partly mask a negative relationship between neighbourhood satisfaction and suicide among men. When health is not an immediate concern, the neighbourhood context may stand out more clearly as a source of stress, low social status or frustration. Prior research indicates that women tend to benefit more from social support in relation to depressive symptoms, whereas men are more strongly affected by material conditions and status-related factors [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], which could help explain why neighbourhood satisfaction matters primarily for men in good health in our data. It is also important to note that our measure of neighbourhood quality is subjective; mental health can bias perceptions, and depression has been shown to be associated with a more pessimistic view of one\u0026rsquo;s environment [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur results suggest that when individuals live in poor-quality housing, neighbourhood advantages do not buffer suicide risk \u003cb\u003e(H3 is supported).\u003c/b\u003e In other words, a mismatch between low housing quality and higher neighbourhood satisfaction is not associated with lower suicide hazards and may even coincide with elevated risk. By contrast, higher levels of neighbourhood satisfaction appear to be associated with lower suicide risks among individuals living in good-quality housing, which points to the detrimental impact of a low-quality close environment even when the broader neighbourhood is perceived positively. For people in poor-quality housing, neighbourhood satisfaction does not alleviate the elevated suicide risk linked to poor housing. Individuals in such situations may experience relative deprivation and downward (self-)comparison, and the contrast between indoor and outdoor conditions may adversely affect mental health [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. According to the Fundamental Cause theory [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], structural disadvantages such as poor housing may not be offset by contextual resources like better neighbourhood quality. Mechanisms of exclusion can also weaken the social ties of people whose housing conditions fall below what is locally expected [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], which is detrimental for mental health [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Finally, selection into discordant situations is likely: downward social mobility may lead to poor-quality housing in otherwise well-regarded neighbourhoods, and such mobility is itself a major determinant of suicide [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImplications\u003c/p\u003e \u003cp\u003eTaken together, our findings underline the central role of housing conditions in suicide prevention. At the population level, even the modest relative differences we observe between housing quality groups may translate into substantial numbers of deaths averted if basic dwelling standards are improved. Policies that focus primarily on neighbourhood regeneration, aesthetics or service provision without tackling poor housing quality risk leaving the most vulnerable groups behind and may even exacerbate perceived inequalities. Our results suggest that priority should be given to ensuring access to safe, comfortable and adequately equipped housing, for example through targeted renovation programmes, minimum quality standards and support for households in the worst dwellings. Integrating housing indicators into suicide prevention strategies and mental health services \u0026ndash; such as outreach or screening in areas with a high concentration of substandard housing \u0026ndash; could help identify people at elevated risk. Although our data refer to early-2000s Belgium, these mechanisms are likely to remain relevant in contemporary contexts where housing precarity and energy poverty are growing concerns. These findings reinforce calls within public health to address material living conditions, and not only individual-level risk factors, in comprehensive suicide prevention strategies.\u003c/p\u003e \u003cp\u003eStrengths and limitations\u003c/p\u003e \u003cp\u003eOur study has several strengths. First, it relies on a high-quality, population-based dataset. The 2001 Belgian Census covers almost the entire resident population, with a very high response rate and rich information on indoor housing characteristics, satisfaction with the immediate environment and subjective health. The 2001 Census remains one of the most detailed and underused sources on housing and neighbourhood quality in Belgium, even though it now refers to an earlier period. We used simple group-based imputations for the housing and neighbourhood scores to reduce selection bias due to missing data, and robustness checks suggest that this did not materially affect the results. A second strength is the construction of two composite scores for housing and neighbourhood quality based on principal component analysis and grouped into halves, thirds or quartiles. This approach is transparent and easily replicable, encouraging researchers in other countries with suitable data to reproduce and extend our analyses.\u003c/p\u003e \u003cp\u003eThe Belgian context offers an additional advantage for interpretation. Belgium has long had suicide rates above the European average, while housing conditions have been comparatively good since the 2000s, with a proportion of overcrowded homes well below the European average [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] and a relatively high rate of homeownership [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], often associated with better housing quality. This means our study is conducted in a setting where extremely poor housing conditions and very pronounced housing inequalities are relatively rare, and where debates about a housing \u0026ldquo;crisis\u0026rdquo; have sometimes been considered exaggerated [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Future work could replicate this analysis in contexts with more marked housing inequalities to examine whether greater prevalence and intensity of poor living environments further increase suicide risk among the most vulnerable, or whether widespread disadvantage changes the role of relative deprivation and social stigma.\u003c/p\u003e \u003cp\u003eOur study also has limitations. First, it cannot establish a causal relationship between environmental conditions and suicide mortality. We observe living conditions at a single point in time, through the 2001 Census, and do not have information on housing and neighbourhood trajectories over the life course. With repeated measures of housing quality and neighbourhood satisfaction, it would be possible to better assess temporality and strengthen causal claims. We partially address this issue by re-estimating the models in the subsample of individuals who reported good or very good subjective health at baseline (Appendix, Table A6). This helps to control for a major confounding factor \u0026ndash; poor physical and mental health \u0026ndash; which is strongly associated with both housing conditions and suicide risk. The comparison between the full and restricted samples highlights where associations are largely confounded by predisposition to poor health.\u003c/p\u003e \u003cp\u003eSecond, mortality from other causes and residential mobility may not be neutral censoring events. In the main Cox models, we assume that death from other causes (including causes related to substance use) and residential mobility are independent of suicide risk. Yet our findings show that short residence duration (less than one year) is associated with higher suicide risk than longer residence, suggesting that residential change and suicide may be related. We therefore estimated Fine-Gray subdistribution hazard models, treating death from other causes and residential mobility as competing events rather than censoring (Appendix, Tables A7-A8). These models yield results very similar to the Cox models for the associations between living conditions and suicide, suggesting that informative censoring due to mortality or mobility is unlikely to bias the estimated relationship between housing conditions and suicide risk.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe contribute to the existing literature by quantifying the association between housing quality and suicide, independently of education, occupation, marital status and other individual characteristics. In early-2000s Belgium, this association is clear across the working-age population, underscoring the importance of policies that guarantee access to adequate housing, including essential facilities, energy-efficient renovations and maintenance of basic installations. Our results highlight the primacy of the home environment in relation to suicide risk: improvements in broader neighbourhood conditions appear to offer protective effects only when basic housing adequacy is first ensured. These findings add to a growing body of work calling for integrated housing and mental health policies, with particular attention to the most deprived housing sectors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe study is based on pseudonymised census and register data provided by Statistics Belgium. The use of these data for scientific research was approved by Statistics Belgium ethical board. In line with Belgian regulations on secondary use of administrative data, individual informed consent was not required.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials.\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;The micro-level census and register data analysed during the current study are not publicly available due to legal restrictions on data confidentiality. Access can be requested from Statistics Belgium under strict conditions for accredited researchers. The authors are not permitted to share the data directly.\u003c/p\u003e\n\u003ch2\u003eCompeting interest.\u003c/h2\u003e\n\u003cp\u003eThe author declares no competing interest.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe author has received an FNRS Fellow researcher mandate to conduct this research.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eContribution\u003c/h2\u003e\n\u003cp\u003eJD conducted and coordinated the whole study, which includes the selection of the design, the data preparation and analysis, the visualization and interpretation of the results and the manuscript writing.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAcknowledgment\u003c/h2\u003e\n\u003cp\u003eThe study uses data from Statbel (Directorate-general Statistics \u0026ndash; Statistics Belgium) \u0026ndash; Demobel (adaptation of the National Register). Computational resources have been provided by the supercomputing facilities of the Universit\u0026eacute; Catholique de Louvain (CISM/UCL) and the Consortium des Equipements de Calcul Intensif en F\u0026eacute;d\u0026eacute;ration Wallonie-Bruxelles (CECI) funded by the Fond de la Recherche Scientifique de Belgique (FRS-FNRS) under convention 2.5020.11 and by the Walloon Region. The author thanks Statistics Belgium and, particularly, Patrick Lusyne and Clo\u0026euml; Ost for preparing and anonymising the dataset. Open access funded by the Library of the University of Helsinki. The author thanks Thierry Eggerickx, Christine Schnor and Bruno Masquelier for valuable feedback.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEurostat. Causes of death - standardised death rate [Internet]. 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[cited 2025 July 9]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-031-87267-9_2\u003c/span\u003e\u003cspan address=\"10.1007/978-3-031-87267-9_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Housing conditions, Suicide, Neighbourhood quality, Register data, Belgium","lastPublishedDoi":"10.21203/rs.3.rs-8338834/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8338834/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHousing and neighbourhood conditions are increasingly recognised as structural determinants of mental health, yet their role in suicide mortality remains poorly quantified. We examined how housing quality and neighbourhood satisfaction are associated with suicide risk in the Belgian working-age population, and whether these associations vary across life stages or according to concordance between housing and neighbourhood conditions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used linked data from the 2001 Belgian Census, the National Register and cause-of-death records to construct a population-based cohort of all adults aged 25\u0026ndash;69 years (N\u0026thinsp;=\u0026thinsp;2.8\u0026nbsp;million) followed for suicide mortality between 2002 and 2006. Housing and neighbourhood quality scores were derived using principal component analysis from detailed census items on dwelling characteristics, installations and satisfaction with the immediate environment and local services. We estimated sex-specific Cox proportional hazards models for suicide, adjusting for demographic and socioeconomic characteristics, subjective health, residential duration and municipal deprivation. We tested interactions with age group and neighbourhood satisfaction and conducted robustness checks excluding imputed data, restricting to respondents in good self-rated health, and applying Fine-Gray competing-risks models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared with living in the lowest quartile of housing quality, residence in the highest quartile was associated with a 13% lower suicide hazard among men and a 21% lower hazard among women, net of individual and contextual covariates. Neighbourhood satisfaction showed only weak overall associations with suicide risk. However, among individuals living in good-quality housing, higher neighbourhood satisfaction was associated with lower suicide hazards, whereas among those in poor-quality housing, neighbourhood satisfaction did not attenuate the elevated suicide risk. We found no clear evidence that associations between housing quality and suicide differed across adult life stages. Robustness analyses yielded similar patterns.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn early-2000s Belgium, poor housing conditions were strongly associated with higher suicide mortality, independently of conventional sociodemographic risk factors. Neighbourhood advantages appeared protective only when basic housing adequacy was ensured, and did not compensate for poor housing. Suicide prevention and mental health policy should therefore consider housing quality as a key determinant, alongside efforts to improve neighbourhood environments.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003enot applicable\u003c/p\u003e","manuscriptTitle":"Housing Conditions, Neighbourhood Satisfaction and Suicide Mortality in Belgium: A Population-Based Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 05:03:55","doi":"10.21203/rs.3.rs-8338834/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70006484-9420-4c45-9937-b50bb4b6726d","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T18:09:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 05:03:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8338834","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8338834","identity":"rs-8338834","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}