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Background: Suicide risk is complex and nuanced, and how place impacts suicide risk when considered alongside detailed individual risk factors remains uncertain. We aimed to examine suicide risk in Denmark with both individual and neighbourhood level risk factors. Methods: We used Danish register-based data to identify individuals born in Denmark from 1972, with full parental information and psychiatric diagnosis history. We fitted a two-level survival model to estimate individual and neighbourhood determinants on suicide risk. Results: We identified 1723 cases of suicide in Denmark during the follow-up period from 1982 to 2015. Suicide risk was explained mainly by individual determinants. Parental comorbidities, particularly maternal schizophrenia [incidence rate ratio (IRR): 2.29, 95% CI 1.56–3.16] and paternal death (2.29, 95% CI 1.31–3.72) partly explained suicide risk when adjusted for all other determinants. The general contextual effect of suicide risk across neighbourhoods showed a median incidence rate ratio (MRR) of 1.13 (1.01–1.28), which was further reduced with full adjustment. Suicide risk increased in neighbourhoods with a higher proportion of manual workers (IRR: 1.08; 1.03–1.14), and decreased with a higher population density (IRR: 0.89; 0.83–0.96). Conclusion: Suicide risk varies mainly between individuals, with parental comorbidities having the largest effect on suicide risk. Suicide risk was less impacted by neighbourhood, though, albeit to a lesser extent than individual determinants, some characteristics were associated with suicide risk. Suicide prevention policies might consider targeting interventions towards individuals more vulnerable due to particular parental comorbidities, whilst taking into account that some neighbourhood characteristics might exacerbate this risk further. [ABSTRACT FROM AUTHOR]

Background Urban-rural differences in schizophrenia risk have been widely evidenced across Western countries. However, explanation of these differences is lacking. We aimed to identify contextual risk factors for schizophrenia that explain urban-rural differences in schizophrenia risk. Methods Utilizing Danish population-based registers, we partitioned Denmark into 1885 geographic "neighborhoods" homogeneously sized in terms of population. Information on the entire Danish population from 1981 to 2016 was used to quantify a spectrum of neighborhood-level domains. We subsequently conducted multilevel survival analyses following persons born in Denmark from 1971 to 1982 for the development of schizophrenia allowing for clustering of people within neighborhoods. We used this method to tease apart the effects of individual, specific, and general contextual risk factors for schizophrenia. Results A significant general contextual effect in schizophrenia risk across neighborhoods was estimated (Medium Incidence Rate Ratio (MRR):1.41; 95% CI:1.35–1.48). Most of the specific contextual factors examined were associated with schizophrenia risk. For instance, neighborhood-level proportion of lone adult households (Incidence Rate Ratios (IRR):1.53; 95% CI:1.44–1.63) had largest risk estimate. Adjustment for all individual-level and specific contextual constructs reduced the IRR for urbanicity from 1.98 (95% CI:1.77–2.22) to 1.30 (95% CI:1.11–1.51). Conclusions In the largest prospective multilevel survival analyses of schizophrenia risk conducted to date, multiple neighborhood-level characteristics were associated with raised schizophrenia risk, with these contextual factors explaining most of the elevated risk linked with urbanicity. However, the unexplained heterogeneity that was evident in our multilevel models indicates that our understanding of the role of urbanicity in schizophrenia's etiology remains incomplete. [ABSTRACT FROM AUTHOR]

The association between air pollution and risk of respiratory tract infection (RTI) in adults needs to be clarified in settings with low to moderate levels of air pollution. We investigated this in the Danish population between 2004 and 2016. We included 3 653 490 persons aged 18–64 years in a nested case-control study. Exposure was defined as the average daily concentration at the individual's residential address of CO, NO X , NO 2 , O 3 , SO 2 , NH 3 , PPM 2.5 , black carbon, organic carbon, mineral dust, sea salt, secondary inorganic aerosols, SO 4 2-, NO 3 -, NH 4 +, secondary organic aerosols, PM 2.5 , and PM 10 during a 3-month exposure window. RTIs were defined by hospitalization for RTIs. Incidence rate ratios (IRRs) and 95% CIs were estimated comparing highest with lowest decile of exposure using conditional logistic regression models. In total, 188 439 incident cases of RTI were identified. Exposure to most air pollutants was positively associated with risk of RTI. For example, NO 2 showed an IRR of 1.52 (CI: 1.48–1.55), and PM 2.5 showed an IRR of 1.45 (CI: 1.40–1.50). In contrast, exposure to sea salt, PM 10 , NH 3 , and O 3 was negatively associated with a risk of RTIs. In this nationwide study comprising adults, exposure to air pollution was associated with risk of RTIs and subgroups hereof. Sea salt, PM 10 , NH 3 , and O 3 may be proxies for rural areas, as the levels of these species in Denmark are higher near the western coastlines and/or in rural areas with fewer combustion sources. [ABSTRACT FROM AUTHOR]

Natural environments have been associated with mental health benefits, but globally access to these benefits is threatened by urban development and densification. However, it remains unclear how natural environments relate to mental health and how consistent the association is across populations. Here we use a life-course approach with a population consisting of 66 194 individuals from the Danish Blood Donor Study (DBDS) to investigate the association between green and blue space (e.g. parks and lakes) and self-evaluated mental well-being. Green and blue space was identified from remotely-sensed images from the Landsat program, while mental well-being was based on the mental component score (MCS) calculated using the 12-item short form health survey. We use multivariate linear regression models and logistic regression models to quantify the associations. We adjust for additional environmental (urbanization, and air pollution) and lifestyle factors (smoking, body mass index, socioeconomic status, and physical activity) and specifically evaluate the role of physical activity and air pollution as possible mediating factors. We found a positive association between the MCS and current and childhood green space, and a non-significant association for current and childhood blue space. Adjusting for environmental and the other factors attenuated the effect sizes indicating that a broad range of factors determine mental well-being. Physical activity and air pollution were both associated with the MCS as possible mediators of green space associations. In addition, the odds for successfully completing tasks', seeing others, and feeling less downhearted increased with higher levels of green space, and the odds of feeling calm increased with higher levels of blue space. In conclusion, we found support for an association between green and, to less degree, blue space and mental well-being throughout different life stages. In addition, we found a positive association with individual indicators of mental well-being such as being productive, feeling less downhearted and calmer, and being social. The healthy blood donor effect and the bias towards urban residency may explain why we found smaller effect sizes between green and blue space and mental well-being for this generally healthy and resourceful cohort compared to previous studies. [ABSTRACT FROM AUTHOR]

Landsat imagery is an unparalleled freely available data source that allows reconstructing land-cover and land-use change, including urban form. This paper addresses the challenge of using Landsat data, particularly its 30 m spatial resolution, for monitoring three-dimensional urban densification. Unlike conventional convolutional neural networks (CNNs) for scene recognition resulting in resolution loss, the proposed semantic segmentation framework provides a pixel-wise classification and improves the accuracy of urban form mapping. We compare temporal and spatial transferability of an adapted DeepLab model with a simple fully convolutional network (FCN) and a texture-based random forest (RF) model to map urban density in the two morphological dimensions: horizontal (compact, open, sparse) and vertical (high rise, low rise). We test whether a model trained on the 2014 data can be applied to 2006 and 1995 for Denmark, and examine whether we could use the model trained on the Danish data to accurately map ten other European cities. Our results show that an implementation of deep networks and the inclusion of multi-scale contextual information greatly improve the classification and the model's ability to generalize across space and time. Between the two semantic segmentation models, DeepLab provides more accurate horizontal and vertical classifications than FCN when sufficient training data is available. By using DeepLab, the F1 score can be increased by 4 and 10 percentage points for detecting vertical urban growth compared to FCN and RF for Denmark. For mapping the ten other European cities with training data from Denmark, DeepLab also shows an advantage of 6 percentage points over RF for both horizontal and vertical dimensions. The resulting maps across the years 1985 to 2018 reveal different patterns of urban growth between Copenhagen and Aarhus, the two largest cities in Denmark, illustrating that those cities have used various planning policies in addressing population growth and housing supply challenges. In summary, we propose a transferable deep learning approach for automated, long-term mapping of urban form from Landsat images that is effective in areas experiencing a slow pace of urban growth or with small-scale changes. • A workflow capturing 30 m urban dynamics based on Landsat imagery and deep learning • CNN-based semantic segmentation models outperform random forest approaches. • Spatial and temporal transferability of 3D urban form mapping is feasible. • Urban density growth in Denmark varies between cities and through time 1985–2018. [ABSTRACT FROM AUTHOR]

Natural environments have been associated with mental health benefits worldwide. However, how different elements and types of natural environments associate with mental health is still largely unknown. In this study, we perform a detailed analysis on a large, nation-wide data set of mental health records (908 553 individuals) for Denmark combined with remotely-sensed land cover and vegetation density data. We explore associations between growing up surrounded by different environments and rates of a spectrum of 18 psychiatric disorders. Childhood land cover exposure for urban, agricultural, near-natural green space, and blue space was determined around the residence of each individual. Vegetation density and air pollution were evaluated as potential pathways. Cox proportional hazards models were used to estimate rates as hazard ratios and then adjusted for potential confounding from other known risk factors. For 12 of 18 disorders, rates were lower for children growing up in environments with more natural elements (near-natural green space, blue space, and agriculture) compared to children growing up in urban environments. High vegetation density was associated with lower rates for most disorders within all the examined environments, whereas mitigation of air pollution by natural environments seemed a less important potential pathway. Rates were not notably changed by adjustment for urbanization, parental and municipal socioeconomic status, family history of mental illness, and parents' age. In conclusion, we found that growing up surrounded by a range of natural environments such as near-natural green space, blue space, and agriculture may lower rates of psychiatric disorders. Our results show the importance of ensuring access to natural environments from as nature-based solutions for improved public health and sustainable, livable cities. Image 1 • Psychiatric disorder rates generally increased incrementally with urban land cover. • Rates for near-natural green space decreased incrementally for most disorders. • Air pollution exposure was associated with risk for some disorders (e.g. Schizophrenia). • Land cover association strength was similar to air pollution and family history of mental illness. • Findings have implications for public health and sustainable urban development. [ABSTRACT FROM AUTHOR]

Importance: Complex biological, socioeconomic, and psychological variables combine to cause mental illnesses, with mounting evidence that early-life experiences are associated with adulthood mental health., Objective: To evaluate whether changing neighborhood income deprivation and residential moves during childhood are associated with the risk of receiving a diagnosis of depression in adulthood., Design, Setting, and Participants: This cohort study included the whole population of 1 096 916 people born in Denmark from January 1, 1982, to December 31, 2003, who resided in the country during their first 15 years of life. Individuals were followed up from 15 years of age until either death, emigration, depression diagnosis, or December 31, 2018. Longitudinal data on residential location was obtained by linking all individuals to the Danish longitudinal population register. Statistical analysis was performed from June 2022 to January 2024., Exposures: Exposures included a neighborhood income deprivation index at place of residence for each year from birth to 15 years of age and a mean income deprivation index for the entire childhood (aged ≤15 years). Residential moves were considered by defining "stayers" as individuals who lived in the same data zone during their entire childhood and "movers" as those who did not., Main Outcomes and Measures: Multilevel survival analysis determined associations between neighborhood-level income deprivation and depression incidence rates after adjustment for individual factors. Results were reported as incidence rate ratios (IRRs) with 95% credible intervals (95% CrIs). The hypotheses were formulated before data collection., Results: A total of 1 096 916 individuals (563 864 male participants [51.4%]) were followed up from 15 years of age. During follow-up, 35 098 individuals (23 728 female participants [67.6%]) received a diagnosis of depression. People living in deprived areas during childhood had an increased risk of depression (IRR, 1.10 [95% CrI, 1.08-1.12]). After full individual-level adjustment, the risk was attenuated (IRR, 1.02 [95% CrI, 1.01-1.04]), indicating an increase of 2% in depression incidence for each 1-SD increase in income deprivation. Moving during childhood, independent of neighborhood deprivation status, was associated with significantly higher rates of depression in adulthood compared with not moving (IRR, 1.61 [95% CrI, 1.52-1.70] for 2 or more moves after full adjustment)., Conclusions and Relevance: This study suggests that, rather than just high or changing neighborhood income deprivation trajectories in childhood being associated with adulthood depression, a settled home environment in childhood may have a protective association against depression. Policies that enable and support settled childhoods should be promoted.

Aims: We provide an overview of nationwide environmental data available for Denmark and its linkage potentials to individual-level records with the aim of promoting research on the potential impact of the local surrounding environment on human health., Background: Researchers in Denmark have unique opportunities for conducting large population-based studies treating the entire Danish population as one big, open and dynamic cohort based on nationally complete population and health registries. So far, most research in this area has utilised individual- and family-level information to study the clustering of disease in families, comorbidities, risk of, and prognosis after, disease onset, and social gradients in disease risk. Linking environmental data in time and space to individuals enables novel possibilities for studying the health effects of the social, built and physical environment., Methods: We describe the possible linkage between individuals and their local surrounding environment to establish the exposome - that is, the total environmental exposure of an individual over their life course., Conclusions: The currently available nationwide longitudinal environmental data in Denmark constitutes a valuable and globally rare asset that can help explore the impact of the exposome on human health ., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Dementia is a major global public health concern that is increasingly leading to morbidity and mortality among older adults. While studies have focused on the risk factors and care provision, there is currently limited knowledge about the spatial risk pattern of the disease. In this study, we employ Bayesian spatial modelling with a stochastic partial differential equation (SPDE) approach to model the spatial risk using complete residential history data from the Danish population and health registers. The study cohort consisted of 1.6 million people aged 65 years and above from 2005 to 2018. The results of the spatial risk map indicate high-risk areas in Copenhagen, southern Jutland and Funen. Individual socioeconomic factors and population density reduce the intensity of high-risk patterns across Denmark. The findings of this study call for the critical examination of the contribution of place of residence in the susceptibility of the global ageing population to dementia., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Urban areas are associated with higher depression risks than rural areas. However, less is known about how different types of urban environments relate to depression risk. Here, we use satellite imagery and machine learning to quantify three-dimensional (3D) urban form (i.e., building density and height) over time. Combining satellite-derived urban form data and individual-level residential addresses, health, and socioeconomic registers, we conduct a case-control study ( n = 75,650 cases and 756,500 controls) to examine the association between 3D urban form and depression in the Danish population. We find that living in dense inner-city areas did not carry the highest depression risks. Rather, after adjusting for socioeconomic factors, the highest risk was among sprawling suburbs, and the lowest was among multistory buildings with open space in the vicinity. The finding suggests that spatial land-use planning should prioritize securing access to open space in densely built areas to mitigate depression risks.

Background: A socioeconomically disadvantaged childhood has been associated with elevated self-harm and violent criminality risks during adolescence and young adulthood. However, whether these risks are modified by a neighbourhood's socioeconomic profile is unclear. The aim of our study was to compare risks among disadvantaged young people residing in deprived areas versus risks among similarly disadvantaged individuals residing in affluent areas., Methods: We did a national cohort study, using Danish interlinked national registers, from which we delineated a longitudinal cohort of people born in Denmark between Jan 1, 1981, and Dec 31, 2001, with two Danish-born parents, who were alive and residing in the country when they were aged 15 years, who were followed up for a hospital-treated self-harm episode or violent crime conviction. A neighbourhood affluence indicator was derived based on nationwide income quartiles, with parental income and educational attainment indicating the socioeconomic position of each cohort member's family. Bayesian multilevel survival analyses were done to examine the moderating influences of neighbourhood affluence on associations between family socioeconomic position and sex-specific risks for the two adverse outcomes., Findings: 1 084 047 cohort members were followed up for 12·8 million person-years in aggregate. Individuals of a low socioeconomic position residing in deprived neighbourhoods had a higher incidence of both self-harm and violent criminality compared with equivalently disadvantaged peers residing in affluent areas. Women from a low-income background residing in affluent areas had, on average, 95 (highest density interval 76-118) fewer self-harm episodes and 25 (15-41) fewer violent crime convictions per 10 000 person-years compared with women of an equally low income residing in deprived areas, whereas men of a low income residing in affluent areas had 61 (39-81) fewer self-harm episodes and 88 (56-191) fewer violent crime convictions per 10 000 person-years than men of a low income residing in deprived areas., Interpretation: Even in a high-income European country with comprehensive social welfare and low levels of poverty and inequality, individuals residing in affluent neighbourhoods have lower risks of self-harm and violent criminality compared with individuals residing in deprived neighbourhoods. More research is needed to explore the potential of neighbourhood policies and interventions to reduce the harmful effects of growing up in socioeconomically deprived circumstances on later risk of self-harm and violent crime convictions., Funding: European Research Council, Lundbeck Foundation Initiative for Integrative Psychiatric Research, and BERTHA, the Danish Big Data Centre for Environment and Health funded by the Novo Nordisk Foundation Challenge Programme., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)